Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_bn_eq

CMakeLists.txt

@@ -36,8 +36,7 @@ include(simd)
 ################################ Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
-option(WITH_MKLDNN      "Compile PaddlePaddle with mkl-dnn support."    ${AVX_FOUND})
-option(WITH_MKLML       "Compile PaddlePaddle with mklml package."      ${AVX_FOUND})
+option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
 option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)
@@ -82,10 +81,8 @@ if(ANDROID OR IOS)
         "Disable PYTHON when cross-compiling for Android and iOS" FORCE)
     set(WITH_RDMA OFF CACHE STRING
         "Disable RDMA when cross-compiling for Android and iOS" FORCE)
-    set(WITH_MKLDNN OFF CACHE STRING
-        "Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
-    set(WITH_MKLML OFF CACHE STRING
-        "Disable MKLML package when cross-compiling for Android and iOS" FORCE)
+    set(WITH_MKL OFF CACHE STRING
+        "Disable MKL when cross-compiling for Android and iOS" FORCE)
 
     # Compile PaddlePaddle mobile inference library
     if (NOT WITH_C_API)
@@ -111,6 +108,14 @@ else()
     set(THIRD_PARTY_BUILD_TYPE Release)
 endif()
 
+set(WITH_MKLML ${WITH_MKL})
+if (WITH_MKL AND AVX2_FOUND)
+    set(WITH_MKLDNN ON)
+else()
+    message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
+    set(WITH_MKLDNN OFF)
+endif()
+
 ########################################################################################
 
 include(external/mklml)     # download mklml package
@@ -158,14 +163,15 @@ set(EXTERNAL_LIBS
 )
 
 if(WITH_GPU)
-    list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
-    if(NOT WITH_DSO)
-        list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
-    endif(NOT WITH_DSO)
+  include(cuda)
 endif(WITH_GPU)
 
+if(WITH_MKLML)
+    list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
+endif()
+
 if(WITH_MKLDNN)
-    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
+    list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
 endif()
 
 if(USE_NNPACK)

benchmark/IntelOptimizedPaddle.md

@@ -12,11 +12,11 @@ Machine:
 
 System: CentOS release 6.3 (Final), Docker 1.12.1.
 
-PaddlePaddle: paddlepaddle/paddle:latest (TODO: will rerun after 0.11.0)
-
-- MKL-DNN tag v0.10
-- MKLML 2018.0.20170720
+PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
+- MKL-DNN tag v0.11
+- MKLML 2018.0.1.20171007
 - OpenBLAS v0.2.20
+(TODO: will rerun after 0.11.0)
 	 
 On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
 
@@ -31,15 +31,26 @@ Input image size - 3 * 224 * 224, Time: images/second
 
 | BatchSize    | 64    | 128  | 256     |
 |--------------|-------| -----| --------|
-| OpenBLAS     | 7.82  | 8.62  | 10.34  | 
-| MKLML        | 11.02 | 12.86 | 15.33  |
-| MKL-DNN      | 27.69 | 28.8 | 29.27  |
+| OpenBLAS     | 7.80  | 9.00  | 10.80  | 
+| MKLML        | 12.12 | 13.70 | 16.18  |
+| MKL-DNN      | 28.46 | 29.83 | 30.44  |
+
+
+chart on batch size 128
+TBD
+
+ - ResNet-50
+
+| BatchSize    | 64    | 128   | 256    |
+|--------------|-------| ------| -------|
+| OpenBLAS     | 25.22 | 25.68 | 27.12  | 
+| MKLML        | 32.52 | 31.89 | 33.12  |
+| MKL-DNN      | 81.69 | 82.35 | 84.08  |
 
 
 chart on batch size 128
 TBD
 
- - ResNet
  - GoogLeNet
 
 ### Laptop

benchmark/paddle/image/googlenet.py

@@ -5,6 +5,7 @@ height = 224
 width = 224
 num_class = 1000
 batch_size = get_config_arg('batch_size', int, 128)
+use_gpu = get_config_arg('use_gpu', bool, True)
 
 args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
 define_py_data_sources2(
@@ -16,6 +17,8 @@ settings(
     learning_method=MomentumOptimizer(0.9),
     regularization=L2Regularization(0.0005 * batch_size))
 
+conv_projection = conv_projection if use_gpu else img_conv_layer
+
 def inception2(name, input, channels, \
     filter1,
     filter3R, filter3,
@@ -138,7 +141,7 @@ def inception(name, input, channels, \
     cat = concat_layer(
         name=name,
         input=[cov1, cov3, cov5, covprj],
-        bias_attr=True,
+        bias_attr=True if use_gpu else False,
         act=ReluActivation())
     return cat
 

benchmark/paddle/image/run_mkldnn.sh

@@ -40,6 +40,7 @@ fi
 for use_mkldnn in True False; do
   for batchsize in 64 128 256; do
     train vgg 19 $batchsize $use_mkldnn
-    train resnet 50  $batchsize $use_mkldnn
+    train resnet 50 $batchsize $use_mkldnn
+    train googlenet v1 $batchsize $use_mkldnn
   done
 done

cmake/configure.cmake

@@ -76,27 +76,14 @@ else()
     include_directories(${CUDA_TOOLKIT_INCLUDE})
 endif(NOT WITH_GPU)
 
-if(WITH_MKLDNN)
-    add_definitions(-DPADDLE_USE_MKLDNN)
-    if (WITH_MKLML AND MKLDNN_IOMP_DIR)
-        message(STATUS "Enable Intel OpenMP at ${MKLDNN_IOMP_DIR}")
-        set(OPENMP_FLAGS "-fopenmp")
-        set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
-    else()
-        find_package(OpenMP)
-        if(OPENMP_FOUND)
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
-        else()
-            message(WARNING "Can not find OpenMP."
-                 "Some performance features in MKLDNN may not be available")
-        endif()
-    endif()
-
-endif(WITH_MKLDNN)
+if (WITH_MKLML AND MKLML_IOMP_LIB)
+    message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
+    set(OPENMP_FLAGS "-fopenmp")
+    set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
+    set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
+endif()
 
 set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")

cmake/cuda.cmake

+if(NOT WITH_GPU)
+    return()
+endif()
+
+set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
+set(paddle_known_gpu_archs7 "30 35 50 52")
+set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
+
+######################################################################################
+# A function for automatic detection of GPUs installed  (if autodetection is enabled)
+# Usage:
+#   detect_installed_gpus(out_variable)
+function(detect_installed_gpus out_variable)
+  if(NOT CUDA_gpu_detect_output)
+    set(cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
+
+    file(WRITE ${cufile} ""
+      "#include <cstdio>\n"
+      "int main() {\n"
+      "  int count = 0;\n"
+      "  if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
+      "  if (count == 0) return -1;\n"
+      "  for (int device = 0; device < count; ++device) {\n"
+      "    cudaDeviceProp prop;\n"
+      "    if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
+      "      std::printf(\"%d.%d \", prop.major, prop.minor);\n"
+      "  }\n"
+      "  return 0;\n"
+      "}\n")
+
+    execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "-ccbin=${CUDA_HOST_COMPILER}"
+                    "--run" "${cufile}"
+                    WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
+                    RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE nvcc_out
+                    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+    if(nvcc_res EQUAL 0)
+      # only keep the last line of nvcc_out
+      STRING(REGEX REPLACE ";" "\\\\;" nvcc_out "${nvcc_out}")
+      STRING(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}")
+      list(GET nvcc_out -1 nvcc_out)
+      string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}")
+      set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from detect_installed_gpus tool" FORCE)
+    endif()
+  endif()
+
+  if(NOT CUDA_gpu_detect_output)
+    message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
+    set(${out_variable} ${paddle_known_gpu_archs} PARENT_SCOPE)
+  else()
+    set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
+  endif()
+endfunction()
+
+
+########################################################################
+# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
+# Usage:
+#   select_nvcc_arch_flags(out_variable)
+function(select_nvcc_arch_flags out_variable)
+  # List of arch names
+  set(archs_names "Kepler" "Maxwell" "Pascal" "All" "Manual")
+  set(archs_name_default "All")
+  if(NOT CMAKE_CROSSCOMPILING)
+    list(APPEND archs_names "Auto")
+  endif()
+
+  # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
+  set(CUDA_ARCH_NAME ${archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
+  set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${archs_names} )
+  mark_as_advanced(CUDA_ARCH_NAME)
+
+  # verify CUDA_ARCH_NAME value
+  if(NOT ";${archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
+    string(REPLACE ";" ", " archs_names "${archs_names}")
+    message(FATAL_ERROR "Only ${archs_names} architeture names are supported.")
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(CUDA_ARCH_BIN ${paddle_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
+    set(CUDA_ARCH_PTX "50"                     CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
+    mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
+  else()
+    unset(CUDA_ARCH_BIN CACHE)
+    unset(CUDA_ARCH_PTX CACHE)
+  endif()
+
+  if(${CUDA_ARCH_NAME} STREQUAL "Kepler")
+    set(cuda_arch_bin "30 35")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
+    set(cuda_arch_bin "50")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    set(cuda_arch_bin "60 61")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
+    set(cuda_arch_bin "70")
+  elseif(${CUDA_ARCH_NAME} STREQUAL "All")
+    set(cuda_arch_bin ${paddle_known_gpu_archs})
+  elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
+    detect_installed_gpus(cuda_arch_bin)
+  else()  # (${CUDA_ARCH_NAME} STREQUAL "Manual")
+    set(cuda_arch_bin ${CUDA_ARCH_BIN})
+  endif()
+
+  # remove dots and convert to lists
+  string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX REPLACE "\\." "" cuda_arch_ptx "${CUDA_ARCH_PTX}")
+  string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
+  string(REGEX MATCHALL "[0-9]+"   cuda_arch_ptx "${cuda_arch_ptx}")
+  list(REMOVE_DUPLICATES cuda_arch_bin)
+  list(REMOVE_DUPLICATES cuda_arch_ptx)
+
+  set(nvcc_flags "")
+  set(nvcc_archs_readable "")
+
+  # Tell NVCC to add binaries for the specified GPUs
+  foreach(arch ${cuda_arch_bin})
+    if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
+      # User explicitly specified PTX for the concrete BIN
+      list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
+      list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
+    else()
+      # User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
+      list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
+      list(APPEND nvcc_archs_readable sm_${arch})
+    endif()
+  endforeach()
+
+  # Tell NVCC to add PTX intermediate code for the specified architectures
+  foreach(arch ${cuda_arch_ptx})
+    list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
+    list(APPEND nvcc_archs_readable compute_${arch})
+  endforeach()
+
+  string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
+  set(${out_variable}          ${nvcc_flags}          PARENT_SCOPE)
+  set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
+endfunction()
+
+message(STATUS "CUDA detected: " ${CUDA_VERSION})
+if (${CUDA_VERSION} LESS 7.0)
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
+elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs7})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
+  set(paddle_known_gpu_archs ${paddle_known_gpu_archs8})
+  list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
+  list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
+  # CUDA 8 may complain that sm_20 is no longer supported. Suppress the
+  # warning for now.
+  list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
+endif()
+
+include_directories(${CUDA_INCLUDE_DIRS})
+list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
+if(NOT WITH_DSO)
+    list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
+endif(NOT WITH_DSO)
+
+# setting nvcc arch flags
+select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
+list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
+message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
+
+# Set C++11 support
+set(CUDA_PROPAGATE_HOST_FLAGS OFF)
+
+# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
+# So, don't set these flags here.
+list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
+list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
+list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
+# Set :expt-relaxed-constexpr to suppress Eigen warnings
+list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
+
+if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "Release")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELEASE})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
+    list(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
+endif()
+
+mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
+mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)

cmake/external/mkldnn.cmake

@@ -40,10 +40,9 @@ INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
 
 IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
     SET(MKLDNN_DEPENDS   ${MKLML_PROJECT})
-    SET(MKLDNN_MKLROOT   ${MKLML_ROOT})
-    SET(MKLDNN_IOMP_LIB  ${MKLML_IOMP_LIB})
-    SET(MKLDNN_IOMP_DIR  ${MKLML_LIB_DIR})
-    MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
+    MESSAGE(STATUS "Build MKLDNN with MKLML ${MKLML_ROOT}")
+ELSE()
+    MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
 ENDIF()
 
 SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
@@ -57,15 +56,16 @@ ExternalProject_Add(
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
-    CMAKE_ARGS          -DMKLROOT=${MKLDNN_MKLROOT}
+    CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}
     CMAKE_ARGS          -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
     CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
     CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-                        -DMKLROOT:PATH=${MKLDNN_MKLROOT}
+                        -DMKLROOT:PATH=${MKLML_ROOT}
 )
 
 ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
 SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
 ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
-MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
+MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
+add_definitions(-DPADDLE_USE_MKLDNN)
 LIST(APPEND external_project_dependencies mkldnn)

cmake/flags.cmake

@@ -149,58 +149,3 @@ endforeach()
 foreach(flag ${GPU_COMMON_FLAGS})
     safe_set_nvflag(${flag})
 endforeach()
-
-
-set(CUDA_PROPAGATE_HOST_FLAGS OFF)
-
-# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
-# So, don't set these flags here.
-LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
-LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math)
-
-if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "Release")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELEASE})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
-elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
-    LIST(APPEND CUDA_NVCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
-endif()
-
-function(specify_cuda_arch cuda_version cuda_arch)
-    if(${cuda_version} VERSION_GREATER "8.0")
-        foreach(capability 61 62)
-          if(${cuda_arch} STREQUAL ${capability})
-            list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
-          endif()
-        endforeach()
-    elseif(${cuda_version} VERSION_GREATER "7.0" and ${cuda_arch} STREQUAL "53")
-        list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
-    endif()
-endfunction()
-
-# Common gpu architectures: Kepler, Maxwell
-foreach(capability 30 35 50)
-      list(APPEND __arch_flags " -gencode arch=compute_${capability},code=sm_${capability}")
-endforeach()
-
-if (CUDA_VERSION VERSION_GREATER "7.0" OR CUDA_VERSION VERSION_EQUAL "7.0")
-      list(APPEND __arch_flags " -gencode arch=compute_52,code=sm_52")
-endif()
-
-# Modern gpu architectures: Pascal
-if (CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0")
-      list(APPEND __arch_flags " -gencode arch=compute_60,code=sm_60")
-      list(APPEND CUDA_NVCC_FLAGS --expt-relaxed-constexpr)
-endif()
-
-# Custom gpu architecture
-set(CUDA_ARCH)
-
-if(CUDA_ARCH)
-  specify_cuda_arch(${CUDA_VERSION} ${CUDA_ARCH})
-endif()
-
-set(CUDA_NVCC_FLAGS ${__arch_flags} ${CUDA_NVCC_FLAGS})
-

cmake/util.cmake

@@ -115,8 +115,8 @@ function(link_paddle_exe TARGET_NAME)
         target_link_libraries(${TARGET_NAME} log)
     endif(ANDROID)
 
-    if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
-      target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
+    if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
+      target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
     endif()
 
     add_dependencies(${TARGET_NAME} ${external_project_dependencies})

doc/api/v2/config/layer.rst

@@ -335,6 +335,16 @@ bilinear_interp
 ..  autoclass:: paddle.v2.layer.bilinear_interp
     :noindex:
 
+dot_prod
+---------
+.. autoclass:: paddle.v2.layer.dot_prod
+    :noindex:
+
+out_prod
+--------
+.. autoclass:: paddle.v2.layer.out_prod
+    :noindex:
+
 power
 -----
 ..  autoclass:: paddle.v2.layer.power
@@ -372,6 +382,11 @@ cos_sim
 ..  autoclass:: paddle.v2.layer.cos_sim
     :noindex:
 
+l2_distance
+-----------
+..  autoclass:: paddle.v2.layer.l2_distance
+    :noindex:
+
 trans
 -----
 ..  autoclass:: paddle.v2.layer.trans

doc/design/mkldnn/README.MD

@@ -36,13 +36,13 @@ Figure 1. PaddlePaddle on IA.
 我们把集成方案大致分为了如下几个方面。
 
 ### CMake
-我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
+我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
 
-同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
+当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
 
-所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
+当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
 
-**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
+所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
 
 ### Layers
 所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在

doc/howto/dev/write_docs_cn.rst

@@ -34,7 +34,7 @@ PaddlePaddle的文档构建有两种方式。
     cd TO_YOUR_PADDLE_CLONE_PATH
     mkdir -p build
     cd build
-    cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+    cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
     make gen_proto_py
     make paddle_docs paddle_docs_cn
 

paddle/framework/backward.cc

@@ -513,19 +513,14 @@ ParamGradInfoMap AppendBackward(
   const int root_block_idx = 0;
   auto root_block = program_desc.MutableBlock(root_block_idx);
 
-  // insert fill one op for target
-  // TODO(qiao) add some check to the target.
   std::string fill_one_op_out = GradVarName(target.Name());
-  std::vector<int64_t> target_shape_desc = target.Shape();
-  std::vector<int> target_shape;
-  std::transform(target_shape_desc.begin(), target_shape_desc.end(),
-                 std::back_inserter(target_shape),
-                 [](int64_t dim) { return static_cast<int>(dim); });
+  bool is_scalar = target.Shape() == std::vector<int64_t>{1};
+  PADDLE_ENFORCE(is_scalar, "target should be scalar");
   VLOG(3) << "backward from loss=" << target.Name()
           << " data_type=" << target.GetDataType();
   std::unique_ptr<OpDescBind> fill_one_op(
       new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
-                     {{"shape", target_shape},
+                     {{"shape", std::vector<int>{1}},
                       {"value", static_cast<float>(1.0)},
                       {"data_type", target.GetDataType()}}));
   // infer var type of fill_one_op

paddle/framework/backward_test.cc

@@ -508,6 +508,7 @@ TEST(Backward, simple_single_op) {
   op->SetOutput("Out", {"out"});
 
   auto target = f::VarDescBind("out");
+  target.SetShape({1});
   auto var_to_grad = AppendBackward(program, target, {});
 
   ASSERT_EQ(block->AllOps().size(), 3UL);
@@ -544,6 +545,7 @@ TEST(Backward, default_attribute) {
   op->CheckAttrs();
 
   auto target = f::VarDescBind("out");
+  target.SetShape({1});
   AppendBackward(program, target, {});
 
   ASSERT_EQ(block->AllOps().size(), 3UL);
@@ -581,6 +583,7 @@ TEST(Backward, simple_mult_op) {
   op3->SetOutput("Out", {"out3"});
 
   auto target = f::VarDescBind("out3");
+  target.SetShape({1});
   size_t forward_len = block->AllOps().size();
   auto var_to_grad = AppendBackward(program, target, {});
 
@@ -670,6 +673,7 @@ TEST(Backward, intermedia_var_no_grad) {
   op4->SetOutput("Out", {"out4"});
 
   auto target = f::VarDescBind("out4");
+  target.SetShape({1});
   size_t forward_len = block->AllOps().size();
   auto var_to_grad = AppendBackward(program, target, {"out3"});
 
@@ -730,6 +734,7 @@ TEST(Backward, var_no_grad) {
   op2->SetOutput("Z", {"z2"});
 
   auto target = f::VarDescBind("z2");
+  target.SetShape({1});
   size_t forward_len = block->AllOps().size();
   auto var_to_grad = AppendBackward(program, target, {"z1"});
 
@@ -810,6 +815,7 @@ TEST(Backward, shared_var) {
   op3->SetOutput("Out", {"out3"});
 
   auto target = f::VarDescBind("out3");
+  target.SetShape({1});
   size_t forward_len = block->AllOps().size();
   auto var_to_grad = AppendBackward(program, target, {});
 
@@ -888,6 +894,7 @@ TEST(Backward, half_backward) {
   op1->SetOutput("Out", {"out"});
 
   auto target = f::VarDescBind("out");
+  target.SetShape({1});
   size_t forward_len = block->AllOps().size();
   auto var_to_grad = AppendBackward(program, target, {"b"});
   f::OpDescBind *fill_op = block->AllOps()[forward_len];

paddle/framework/data_type.h

@@ -46,6 +46,8 @@ inline std::type_index ToTypeIndex(DataType type) {
       return typeid(int);
     case DataType::INT64:
       return typeid(int64_t);
+    case DataType::BOOL:
+      return typeid(bool);
     default:
       PADDLE_THROW("Not support type %d", type);
   }
@@ -66,6 +68,9 @@ inline void VisitDataType(DataType type, Visitor visitor) {
     case DataType::INT64:
       visitor.template operator()<int64_t>();
       break;
+    case DataType::BOOL:
+      visitor.template operator()<bool>();
+      break;
     default:
       PADDLE_THROW("Not supported");
   }

paddle/gserver/CMakeLists.txt

@@ -73,7 +73,6 @@ if(MOBILE_INFERENCE)
     list(REMOVE_ITEM GSERVER_SOURCES
          dataproviders/DataProvider.cpp
          dataproviders/MultiDataProvider.cpp
-         dataproviders/ProtoDataProvider.cpp
          dataproviders/PyDataProvider2.cpp
          dataproviders/PyDataProvider.cpp)
 

paddle/gserver/activations/ActivationFunction.cpp

@@ -212,6 +212,37 @@ Error __must_check backward(Argument& act) {
 }
 END_DEFINE_ACTIVATION(sequence_softmax)
 
+/*
+ * @brief SoftSign Activation.
+ * \f[
+ * f(z) = \frac{z}{1 + |z|}
+ * \f]
+ */
+BEGIN_DEFINE_ACTIVATION(softsign)
+private:
+MatrixPtr denominator_;
+
+Error __must_check forward(Argument& act) {
+  size_t height = act.value->getHeight();
+  size_t width = act.value->getWidth();
+  Matrix::resizeOrCreate(
+      denominator_, height, width, false, useGpu(act.deviceId));
+  denominator_->assign(*act.value);
+  denominator_->abs2();
+  denominator_->add(1.);
+
+  act.value->dotDiv(*act.value, *denominator_);
+  return Error();
+}
+
+Error __must_check backward(Argument& act) {
+  denominator_->square2();
+  denominator_->scalarDiv(*denominator_, 1.);
+  act.grad->dotMul(*act.grad, *denominator_);
+  return Error();
+}
+END_DEFINE_ACTIVATION(softsign)
+
 /**
  * @brief Relu Activation.
  * forward. y = max(0, z)

paddle/gserver/dataproviders/DataProvider.cpp

@@ -16,8 +16,8 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <algorithm>
-#include "ProtoDataProvider.h"
 #include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
 #include "paddle/utils/StringUtil.h"
 #include "paddle/utils/Util.h"
 
@@ -164,8 +164,6 @@ DataProvider* DataProvider::create(const DataConfig& config,
 
 REGISTER_DATA_PROVIDER(simple, SimpleDataProvider);
 REGISTER_DATA_PROVIDER(dummy, DummyDataProvider);
-REGISTER_DATA_PROVIDER(proto, ProtoDataProvider);
-REGISTER_DATA_PROVIDER(proto_sequence, ProtoSequenceDataProvider);
 
 int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
   int64_t batchSize = doubleBuffer_ ? getNextBatchFromBuffer(size, batch)

paddle/gserver/dataproviders/ProtoDataProvider.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <vector>
-
-#include "DataFormat.pb.h"
-#include "paddle/utils/Stat.h"
-
-#include "DataProvider.h"
-#include "ProtoReader.h"
-
-namespace paddle {
-
-/**
- * @brief Provider data from protobuf data file with each sample
- * specified by proto message
- *
- * DataSample defined in DataFormat.proto.
- *
- * The file format is
- *
- *    header
- *
- *    sample1
- *
- *    sample2
- *
- *    ...
- *
- *    sampleN
- *
- * @note: In the data file, each message is prefixed with its length.
- * The read/write of the protbuf are implemented in ProtoReader.h
- */
-class ProtoDataProvider : public DataProvider {
-public:
-  ProtoDataProvider(const DataConfig& config,
-                    bool useGpu,
-                    bool loadDataAll = true);
-  virtual void reset();
-
-  /**
-   * @note this size includes the sequences which are skipped because they
-   * are longer than the batch size.
-   */
-  virtual int64_t getSize() {
-    int64_t size = sampleNums_;
-    if (usageRatio_ < 1.0f) {
-      size = static_cast<int64_t>(size * usageRatio_);
-    }
-    return size;
-  }
-  virtual void shuffle();
-
-  void loadData(const std::vector<std::string>& fileList);
-
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-
-protected:
-  /**
-   * @brief load protobuf data from a list of file
-   * @param[in]  fileName  file name of a file which contains
-   * a list of file names
-   */
-  void loadData(const std::string& fileName);
-
-  /**
-   * @brief load protobuf data from file
-   * @param[in]  fileName   data file name
-   */
-  void loadDataFile(const std::string& fileName);
-  /** @brief check data header of each data sample
-   *  @param[in] header     data header read from protobuf data
-   */
-  void checkDataHeader(const DataHeader& header);
-  /**
-   * @brief fill protobuf data into slot_,
-   * slot_ is a vector of ProtoSlot in memory.
-   * @param[in]  sample     data sample read from protobuf data
-   */
-  void fillSlots(const DataSample& sample);
-
-  /**
-   * @brief return true if each sample is one sequence, i.e., independent
-   * of other samples.
-   */
-  inline bool iidData() const { return sequenceStartPositions_.empty(); }
-
-  /**
-   * @brief check that sample is consistent with header_
-   */
-  void checkSample(const DataSample& sample);
-
-  template <class Op>
-  int64_t sequenceLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t sampleLoop(Op op, int64_t size);
-
-  template <class Op>
-  int64_t subSampleLoop(Op op, int64_t size, int slot);
-
-  void showDataStats();
-
-protected:
-  struct ProtoVarSlot {
-    std::vector<real> data;
-    std::vector<int> dims;
-  };
-
-  struct ProtoSlot {
-    SlotDef::SlotType type;
-    int dim;
-    std::vector<int> indexData;
-    std::vector<real> denseData;
-    std::vector<sparse_non_value_t> sparseNonValueData;
-    std::vector<sparse_float_value_t> sparseFloatValueData;
-    std::vector<int64_t> indices;
-    std::vector<int64_t> subIndices;
-
-    std::vector<ProtoVarSlot> varDenseData;
-    std::vector<std::vector<int>> varIndices;
-    std::vector<std::string> strData;
-  };
-  DataHeader header_;
-  int numVecSlots_;
-
-  std::vector<ProtoSlot> slots_;
-  size_t sampleNums_;
-
-  /**
-   * The starting position of each sequence in samples.
-   * The last element should be num of samples.
-   * If empty, each sample is one sequence.
-   */
-  std::vector<size_t> sequenceStartPositions_;
-
-  int64_t currentSequenceIndex_;
-
-  // The size should be the number of sequences.
-  std::vector<size_t> shuffledSequenceIds_;
-
-  ThreadLocalD<DataBatch> cpuBatch_;
-  ThreadLocalD<DataBatch> gpuBatch_;
-
-  RWLock lock_;
-  std::vector<StatPtr> nnzStats_;  // stats for number of none-zeros entries
-};
-
-/**
- * @brief Special use for Proto data: instances should contain sparse-non-value
- * slots
- * and label.
- *
- * @note ProtoSequenceDataProvider treats each SPARSE SLOT as a SEQUENCE
- */
-class ProtoSequenceDataProvider : public ProtoDataProvider {
-public:
-  ProtoSequenceDataProvider(const DataConfig& config,
-                            bool useGpu,
-                            bool loadDataAll = true);
-  ~ProtoSequenceDataProvider() {}
-  virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
-};
-
-}  // namespace paddle

paddle/gserver/layers/DotProdLayer.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "Layer.h"
+#include "paddle/math/Matrix.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+/**
+ * @brief A layer for computing the dot product of two vectors.
+ * Input1: vector (batchSize * dim)
+ * Input2: vector (batchSize * dim)
+ * Output: a matrix: (batchSize * 1)
+ */
+
+class DotProdLayer : public Layer {
+public:
+  explicit DotProdLayer(const LayerConfig& config) : Layer(config) {}
+
+  ~DotProdLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+REGISTER_LAYER(dot_prod, DotProdLayer);
+
+bool DotProdLayer::init(const LayerMap& layerMap,
+                        const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(inputLayers_.size(), 2U);
+  CHECK_EQ(1UL, getSize())
+      << "The output dimensionality of this layer should be fixed to 1.";
+
+  return true;
+}
+
+void DotProdLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  MatrixPtr inV0 = getInputValue(0);
+  MatrixPtr inV1 = getInputValue(1);
+
+  size_t batchSize = inV0->getHeight();
+  CHECK_EQ(inV1->getHeight(), batchSize);
+  CHECK_EQ(inV0->getWidth(), inV1->getWidth());
+
+  {
+    REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
+    reserveOutput(batchSize, 1);
+  }
+
+  MatrixPtr outV = getOutputValue();
+  {
+    REGISTER_TIMER_INFO("FwDotProdTimer", getName().c_str());
+    outV->sumOfProducts(*inV0, *inV1, 1, 0);
+  }
+}
+
+void DotProdLayer::backward(const UpdateCallback& callback) {
+  MatrixPtr inV0 = getInputValue(0);
+  MatrixPtr inV1 = getInputValue(1);
+  MatrixPtr outG = getOutputGrad();
+  MatrixPtr inG0 = getInputGrad(0);
+  MatrixPtr inG1 = getInputGrad(1);
+
+  {
+    REGISTER_TIMER_INFO("BwDotProdTimer", getName().c_str());
+
+    if (inG0) {
+      inG0->addRowScale(0, *inV1, *outG);
+    }
+
+    if (inG1) {
+      inG1->addRowScale(0, *inV0, *outG);
+    }
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/L2DistanceLayer.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "L2DistanceLayer.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+REGISTER_LAYER(l2_distance, L2DistanceLayer);
+
+bool L2DistanceLayer::init(const LayerMap& layerMap,
+                           const ParameterMap& parameterMap) {
+  /* Initialize the basic parent class */
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(inputLayers_.size(), 2UL) << "The L2DistanceLayer accepts two and "
+                                     << "only two inputs.";
+  CHECK_EQ(getSize(), 1UL) << "The output dimensionality of L2DistanceLayer "
+                           << "is fixed to be 1.";
+
+  return true;
+}
+
+void L2DistanceLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  const auto inV1 = getInputValue(0);
+  const auto inV2 = getInputValue(1);
+
+  CHECK(inV1 && inV2);
+  CHECK_EQ(inV1->getHeight(), inV2->getHeight())
+      << "The height of two inputs of this layer must be the same.";
+  CHECK_EQ(inV1->getWidth(), inV2->getWidth())
+      << "The width of two inputs of this layer must be the same.";
+
+  int batchSize = inV1->getHeight();
+  int output_dim = getSize();
+  {
+    REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
+    reserveOutput(batchSize, output_dim);
+    auto outV = getOutputValue();
+    CHECK(outV) << "The output matrix should not be null.";
+
+    Matrix::resizeOrCreate(
+        inputSub_, inV1->getHeight(), inV1->getWidth(), false, useGpu_);
+
+    inputSub_->assign(*inV1);
+    inputSub_->sub(*inV2);
+    outV->sumOfProducts(*inputSub_, *inputSub_, 1, 0);
+    outV->sqrt2(*outV);
+  }
+}
+
+void L2DistanceLayer::backward(const UpdateCallback& callback) {
+  const auto outG = getOutputGrad();
+  const auto outV = getOutputValue();
+  CHECK(outG && outV);
+
+  auto inGrad1 = getInputGrad(0);
+  auto inGrad2 = getInputGrad(1);
+
+  {
+    REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
+
+    if (inGrad1 || inGrad2) {
+      outV->scalarDiv(*outV, 1.);
+      outV->dotMul(*outG, *outV);
+    }
+
+    if (inGrad1) inGrad1->addRowScale(0, *inputSub_, *outV);
+
+    if (inGrad2) {
+      inputSub_->mulScalar(-1.);
+      inGrad2->addRowScale(0, *inputSub_, *outV);
+    }
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/L2DistanceLayer.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "Layer.h"
+#include "paddle/math/Matrix.h"
+
+namespace paddle {
+
+/**
+ * @brief The layer calculates the l2 distance between two input vectors.
+ * \f[
+ * f(\bf{x}, \bf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}
+ * \f]
+ *
+ * - Input1: A vector (batchSize * dataDim)
+ * - Input2: A vector (batchSize * dataDim)
+ * - Output: A vector (batchSize * 1)
+ *
+ * The configuration api is: l2_distance_layer.
+ */
+
+class L2DistanceLayer : public Layer {
+public:
+  explicit L2DistanceLayer(const LayerConfig& config) : Layer(config) {}
+  ~L2DistanceLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+
+private:
+  // Store the result of subtracting Input2 from Input1 in forward computation,
+  // which will be reused in backward computation.
+  MatrixPtr inputSub_;
+};
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNAddtoLayer.cpp

@@ -38,12 +38,13 @@ bool MKLDNNAddtoLayer::init(const LayerMap& layerMap,
 }
 
 void MKLDNNAddtoLayer::reshape(
-    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+    int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
   CHECK_EQ(layerSize_, getSize()) << "this layer size can not be changed";
   reshapeInput(bs, ih, iw);
   ic = inputLayers_[0]->getSize() / ih / iw;
   CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
-  CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
+  CHECK_EQ(inputLayers_[0]->getOutputValue()->getElementCnt(),
+           (size_t)bs * ic * ih * iw);
   for (size_t i = 0; i < inputLayers_.size(); i++) {
     CHECK_EQ(int64_t(bs), inputLayers_[i]->getOutput().getBatchSize());
     CHECK_EQ(layerSize_, inputLayers_[i]->getSize());
@@ -57,47 +58,43 @@ void MKLDNNAddtoLayer::reshape(
 }
 
 void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
-                                MKLDNNMatrixPtr& in,
-                                MKLDNNMatrixPtr& wgt,
-                                MKLDNNMatrixPtr& bias,
+                                std::vector<MKLDNNMatrixPtr>& inputs,
                                 MKLDNNMatrixPtr& out) {
-  resetFwdBuffers(inVals_, bias, out);
-  in = inVals_[0];
+  resetFwdBuffers(inputs, biasVal_, out);
 
   std::shared_ptr<sum::primitive_desc> fwdPD;
   std::shared_ptr<sum::primitive_desc> biasPD;
-  resetFwdPD(fwdPD, biasPD, inVals_, bias, out);
+  resetFwdPD(fwdPD, biasPD, inputs, biasVal_, out);
 
-  resetFwdPipeline(pipeline, fwdPD, biasPD, inVals_, bias, out);
+  resetFwdPipeline(pipeline, fwdPD, biasPD, inputs, biasVal_, out);
 }
 
 void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
-                                MKLDNNMatrixPtr& in,
-                                MKLDNNMatrixPtr& wgt,
-                                MKLDNNMatrixPtr& bias,
+                                std::vector<MKLDNNMatrixPtr>& inputs,
                                 MKLDNNMatrixPtr& out) {
-  resetBwdBuffers(inGrads_, bias, out);
-  in = inGrads_[0];
+  resetBwdBuffers(inputs, biasGrad_, out);
 
   // backward only need share output grad to input grad
-  for (size_t i = 0; i < inGrads_.size(); i++) {
-    if (inGrads_[i] != nullptr) {
-      inGrads_[i] = out;
-      inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    if (inputs[i] != nullptr) {
+      inputs[i] = out;
+      inputLayers_[i]->getOutputGrad()->setData(inputs[i]->getData());
     }
   }
 
   // backward bias
   bwdBias_ = nullptr;
-  if (bias) {
+  if (biasGrad_) {
     std::vector<float> scales(bs_, 1.0);
-    std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc());
-    auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs);
+    std::vector<memory::primitive_desc> srcPDs(bs_,
+                                               biasGrad_->getPrimitiveDesc());
+    auto biasPD =
+        sum::primitive_desc(biasGrad_->getMemoryDesc(), scales, srcPDs);
     std::vector<primitive::at> srcs;
     for (size_t i = 0; i < grads_.size(); ++i) {
       srcs.push_back(*(grads_[i]));
     }
-    bwdBias_.reset(new sum(biasPD, srcs, *bias));
+    bwdBias_.reset(new sum(biasPD, srcs, *biasGrad_));
     pipeline.push_back(*bwdBias_);
   }
 }
@@ -208,7 +205,7 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
 
   inputs.resize(inputLayers_.size());
   for (size_t i = 0; i < inputs.size(); i++) {
-    resetInGrad(inputs[i], inVal_->getPrimitiveDesc(), i);
+    resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
     CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
   }
 

paddle/gserver/layers/MKLDNNAddtoLayer.h

@@ -26,9 +26,6 @@ namespace paddle {
  */
 class MKLDNNAddtoLayer : public MKLDNNLayer {
 protected:
-  std::vector<MKLDNNMatrixPtr> inVals_;
-  std::vector<MKLDNNMatrixPtr> inGrads_;
-
   // layer size == ic * ih * iw == oc * oh *ow, and can not be changed
   size_t layerSize_;
 
@@ -50,52 +47,19 @@ public:
             const ParameterMap& parameterMap) override;
 
   void reshape(
-      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
 
   void resetFwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void resetBwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void updateWeights(const UpdateCallback& callback) override;
 
-  void printValueFormat() override {
-    for (size_t i = 0; i < inVals_.size(); ++i) {
-      VLOG(MKLDNN_FMTS) << i << " input: " << inVals_[i]->getFormat() << " >>>";
-    }
-    if (outVal_) {
-      VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
-    }
-    if (extOutVal_) {
-      VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
-    }
-  }
-
-  void printGradFormat() override {
-    if (extOutGrad_) {
-      VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
-    }
-    if (outGrad_) {
-      VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
-    }
-    for (size_t i = 0; i < inGrads_.size(); ++i) {
-      VLOG(MKLDNN_FMTS) << i << " input: " << inGrads_[i]->getFormat() << "<<<";
-    }
-  }
-
 protected:
-  /**
-   * Forward functions: reset buffers(inputs, output, bias),
-   *                    reset primitive descriptor,
-   *                    reset pipeline.
-   */
   void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
                        MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
@@ -110,17 +74,10 @@ protected:
                         std::vector<MKLDNNMatrixPtr>& inputs,
                         MKLDNNMatrixPtr& bias,
                         MKLDNNMatrixPtr& out);
-
-  /**
-   * Backward functions: reset buffers(inputs, output, bias)
-   */
   void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
                        MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
 
-  /**
-   * prepare for bias
-   */
   void prepareBias(MKLDNNMatrixPtr& bias,
                    const MatrixPtr& biasMat,
                    const MKLDNNMatrixPtr& out,

paddle/gserver/layers/MKLDNNBatchNormLayer.cpp

@@ -116,21 +116,20 @@ void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
 }
 
 void MKLDNNBatchNormLayer::reshape(
-    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+    int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
   reshapeInput(bs, ih, iw);
   oh = ih;
   ow = iw;
   // ic_ and oc can not be changed
-  CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
+  CHECK_EQ((size_t)ic,
+           inputLayers_[0]->getOutputValue()->getElementCnt() / bs / ih / iw)
       << "Input channel can not be changed";
   reshapeOutput(oh, ow);
   resizeOutput(bs, oc * oh * ow);
 }
 
 void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
-                                    MKLDNNMatrixPtr& in,
-                                    MKLDNNMatrixPtr& wgt,
-                                    MKLDNNMatrixPtr& bias,
+                                    std::vector<MKLDNNMatrixPtr>& inputs,
                                     MKLDNNMatrixPtr& out) {
   // In training phase, it will always calculate mean and var,
   // so useGlobalStats must be false.
@@ -140,25 +139,23 @@ void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
     useGlobalStats_ = false;
   }
 
-  resetFwdBuffers(in, wgt, out);
+  resetFwdBuffers(inputs[0], wgtVal_, out);
 
-  resetFwdPD(fwdPD_, in, wgt, out);
+  resetFwdPD(fwdPD_, inputs[0], wgtVal_, out);
 
-  resetFwdPipeline(pipeline, fwdPD_, in, wgt, out);
+  resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, out);
 }
 
 void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
-                                    MKLDNNMatrixPtr& in,
-                                    MKLDNNMatrixPtr& wgt,
-                                    MKLDNNMatrixPtr& bias,
+                                    std::vector<MKLDNNMatrixPtr>& inputs,
                                     MKLDNNMatrixPtr& out) {
   std::shared_ptr<bn_bwd::primitive_desc> pd;
 
-  resetBwdBuffers(in, wgt, out);
+  resetBwdBuffers(inputs[0], wgtGrad_, out);
 
-  resetBwdPD(pd, in, wgt, out);
+  resetBwdPD(pd, inputs[0], wgtGrad_, out);
 
-  resetBwdPipeline(pipeline, pd, in, wgt, out);
+  resetBwdPipeline(pipeline, pd, inputs[0], wgtGrad_, out);
 }
 
 void MKLDNNBatchNormLayer::forward(PassType passType) {
@@ -260,9 +257,9 @@ void MKLDNNBatchNormLayer::resetFwdPipeline(
 void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
                                            MKLDNNMatrixPtr& wgt,
                                            MKLDNNMatrixPtr& out) {
-  CHECK(inVal_ && outVal_);
+  CHECK(inVals_[0] && outVal_);
   resetOutGrad(out, outVal_->getPrimitiveDesc());
-  resetInGrad(in, inVal_->getPrimitiveDesc());
+  resetInGrad(in, inVals_[0]->getPrimitiveDesc());
   if (gradScaleShift_) {
     CHECK(wgtVal_);
     resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc());
@@ -297,11 +294,12 @@ void MKLDNNBatchNormLayer::resetBwdPipeline(
   if (pd == nullptr) {
     return;
   }
-  CHECK(inVal_);
+  CHECK(inVals_[0]);
   bwdData_.reset(
       wgt && wgtVal_
-          ? new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *wgtVal_, *in, *wgt)
-          : new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *in));
+          ? new bn_bwd(
+                *pd, *inVals_[0], *mean_, *var_, *out, *wgtVal_, *in, *wgt)
+          : new bn_bwd(*pd, *inVals_[0], *mean_, *var_, *out, *in));
   pipeline.push_back(*bwdData_);
 }
 

paddle/gserver/layers/MKLDNNBatchNormLayer.h

@@ -74,18 +74,14 @@ public:
   void forward(PassType passType) override;
 
   void reshape(
-      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
 
   void resetFwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void resetBwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void updateWeights(const UpdateCallback& callback) override;
@@ -99,11 +95,7 @@ protected:
    * moving = moving * AvgFraction + local * (1 - AvgFraction)
    */
   void calMovingMeanAndVar();
-  /**
-   * Forward functions: reset buffers(input, weight, output),
-   *                    reset primitive descriptor,
-   *                    reset pipeline.
-   */
+
   void resetFwdBuffers(MKLDNNMatrixPtr& in,
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& out);
@@ -116,12 +108,6 @@ protected:
                         MKLDNNMatrixPtr& in,
                         MKLDNNMatrixPtr& wgt,
                         MKLDNNMatrixPtr& out);
-
-  /**
-   * Backward functions: reset buffers(input, weight, output),
-   *                     reset primitive descriptor,
-   *                     reset pipeline.
-   */
   void resetBwdBuffers(MKLDNNMatrixPtr& in,
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& out);

paddle/gserver/layers/MKLDNNConcatLayer.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "MKLDNNConcatLayer.h"
+
+using namespace mkldnn;  // NOLINT
+typedef memory::format format;
+
+namespace paddle {
+
+REGISTER_LAYER(mkldnn_concat, MKLDNNConcatLayer);
+
+bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
+                             const ParameterMap& parameterMap) {
+  if (!MKLDNNLayer::init(layerMap, parameterMap)) {
+    return false;
+  }
+  CHECK_GT(inputLayers_.size(), 1UL);
+  CHECK(!biasParameter_);
+  return true;
+}
+
+void MKLDNNConcatLayer::reshape(
+    int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
+  reshapeInput(bs, ih, iw);
+  ic = inputLayers_[0]->getSize() / ih / iw;
+  CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
+  CHECK_EQ(inputLayers_[0]->getOutputValue()->getElementCnt(),
+           (size_t)bs * ic * ih * iw);
+  CHECK_GT(inputLayers_.size(), 1UL);
+  channels_.resize(inputLayers_.size());
+  channels_[0] = ic;
+  oc = ic;
+  for (size_t i = 1; i < inputLayers_.size(); i++) {
+    int batchsize, height, witdh;
+    reshapeInput(batchsize, height, witdh, i);
+    CHECK_EQ(bs, batchsize);
+    CHECK_EQ(ih, height);
+    CHECK_EQ(iw, witdh);
+
+    channels_[i] = inputLayers_[i]->getSize() / height / witdh;
+    CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
+    oc += channels_[i];
+  }
+  oh = ih;
+  ow = iw;
+  reshapeOutput(oh, ow);
+  resizeOutput(bs, oc * oh * ow);
+}
+
+void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
+                                 std::vector<MKLDNNMatrixPtr>& inputs,
+                                 MKLDNNMatrixPtr& out) {
+  resetFwdBuffers(inputs, out);
+
+  std::shared_ptr<concat::primitive_desc> fwdPD;
+  resetFwdPD(fwdPD, inputs, out);
+
+  resetFwdPipeline(pipeline, fwdPD, inputs, out);
+}
+
+void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
+                                 std::vector<MKLDNNMatrixPtr>& inputs,
+                                 MKLDNNMatrixPtr& out) {
+  resetBwdBuffers(inputs, out);
+
+  resetBwdPipeline(pipeline, bwds_, inputs, out);
+}
+
+void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                        MKLDNNMatrixPtr& out) {
+  inputs.resize(inputLayers_.size());
+  bool has8c = false, has16c = false, hasnc = false;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    resetInValue(inputs[i], nullptr, i, channels_[i]);
+    CHECK(inputs[i]);
+    auto dm = inputs[i]->getDims();
+    // inputs format can be different, but ndims must equal
+    CHECK(i == 0 || dm.size() == inputs[0]->getDims().size());
+    CHECK_EQ(bs_, dm[0]);
+    CHECK_EQ(channels_[i], dm[1]);
+    if (dm.size() > 2) {
+      CHECK_EQ(ih_, dm[2]);
+      CHECK_EQ(iw_, dm[3]);
+    }
+    if (inputs[i]->getFormat() == format::nc) {
+      hasnc = true;
+    }
+    if (inputs[i]->getFormat() == format::nChw8c) {
+      has8c = true;
+    }
+    if (inputs[i]->getFormat() == format::nChw16c) {
+      has16c = true;
+    }
+  }
+
+  format outFmt;
+  if (has16c && oc_ % 16 == 0) {
+    outFmt = format::nChw16c;
+  } else if (has8c && oc_ % 8 == 0) {
+    outFmt = format::nChw8c;
+  } else if (hasnc) {
+    CHECK(oh_ == 1 && ow_ == 1);
+    outFmt = format::nc;
+  } else {
+    outFmt = format::nchw;
+  }
+  memory::dims outDims =
+      hasnc ? memory::dims{bs_, oc_} : memory::dims{bs_, oc_, oh_, ow_};
+  auto outPD = MKLDNNMatrix::createPrimitiveDesc(outDims, outFmt, engine_);
+  resetOutValue(out, outPD);
+}
+
+void MKLDNNConcatLayer::resetFwdPD(std::shared_ptr<concat::primitive_desc>& pd,
+                                   std::vector<MKLDNNMatrixPtr>& inputs,
+                                   MKLDNNMatrixPtr out) {
+  std::vector<memory::primitive_desc> srcPDs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    srcPDs.push_back(inputs[i]->getPrimitiveDesc());
+  }
+  CHECK(out);
+  pd.reset(new concat::primitive_desc(out->getMemoryDesc(), axis_, srcPDs));
+  CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
+}
+
+void MKLDNNConcatLayer::resetFwdPipeline(
+    std::vector<primitive>& pipeline,
+    std::shared_ptr<concat::primitive_desc>& pd,
+    std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& out) {
+  std::vector<primitive::at> srcs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    srcs.push_back(*(inputs[i]));
+  }
+  fwd_.reset(new concat(*pd, srcs, *out));
+  pipeline.push_back(*fwd_);
+}
+
+void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                                        MKLDNNMatrixPtr& out) {
+  CHECK(outVal_);
+  resetOutGrad(out, outVal_->getPrimitiveDesc());
+  CHECK(out);
+
+  inputs.resize(inputLayers_.size());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    CHECK(inVals_[i]);
+    resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
+    CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
+  }
+}
+
+void MKLDNNConcatLayer::resetBwdPipeline(
+    std::vector<mkldnn::primitive>& pipeline,
+    std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
+    std::vector<MKLDNNMatrixPtr>& inputs,
+    MKLDNNMatrixPtr& out) {
+  // reset the backward primitives
+  memory::dims offsets = {0, 0, 0, 0};
+  prims.resize(inputs.size());
+  CHECK_EQ(inputs.size(), channels_.size());
+  for (size_t i = 0; i < inputs.size(); i++) {
+    auto viewPD = view::primitive_desc(
+        out->getPrimitiveDesc(), inputs[i]->getDims(), offsets);
+    auto bwdPD = reorder::primitive_desc(viewPD.dst_primitive_desc(),
+                                         inputs[i]->getPrimitiveDesc());
+    prims[i].reset(new reorder(bwdPD, *out, *(inputs[i])));
+    offsets[axis_] += channels_[i];
+    // push to pipeline
+    pipeline.push_back(*prims[i]);
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNConcatLayer.h

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "MKLDNNLayer.h"
+#include "mkldnn.hpp"
+
+namespace paddle {
+
+/**
+ * @brief A subclass of MKLDNNLayer Concatenate layer.
+ *
+ * The config file api is mkldnn_concat
+ */
+class MKLDNNConcatLayer : public MKLDNNLayer {
+protected:
+  std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
+  // input channel numbers
+  std::vector<int> channels_;
+
+  // concat_dimension in MKLDNN
+  // if axis_ == 0, concat batchsize
+  // if axis_ == 1, concat channel (default)
+  int axis_;
+
+public:
+  explicit MKLDNNConcatLayer(const LayerConfig& config)
+      : MKLDNNLayer(config), axis_(1) {}
+
+  ~MKLDNNConcatLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void reshape(
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
+
+  void resetFwd(std::vector<mkldnn::primitive>& pipeline,
+                std::vector<MKLDNNMatrixPtr>& inputs,
+                MKLDNNMatrixPtr& out) override;
+
+  void resetBwd(std::vector<mkldnn::primitive>& pipeline,
+                std::vector<MKLDNNMatrixPtr>& inputs,
+                MKLDNNMatrixPtr& out) override;
+
+  void printSizeInfo() override {
+    CHECK_EQ(channels_.size(), inputLayers_.size());
+    for (size_t i = 0; i < channels_.size(); ++i) {
+      VLOG(MKLDNN_SIZES) << "Input " << i << ", " << inputLayers_[i]->getName()
+                         << ": " << bs_ << ", " << channels_[i] << ", " << ih_
+                         << ", " << iw_;
+    }
+    VLOG(MKLDNN_SIZES) << "Output: " << bs_ << ", " << oc_ << ", " << oh_
+                       << ", " << ow_;
+  }
+
+  size_t keepCondition() {
+    // reset when the total element size of all inputs changed
+    size_t totalSize = inputLayers_[0]->getOutputValue()->getElementCnt();
+    for (size_t i = 1; i < inputLayers_.size(); ++i) {
+      totalSize += inputLayers_[i]->getOutputValue()->getElementCnt();
+    }
+    return totalSize;
+  }
+
+protected:
+  void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& out);
+  void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
+                  std::vector<MKLDNNMatrixPtr>& inputs,
+                  MKLDNNMatrixPtr out);
+  void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& out);
+  void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
+                       MKLDNNMatrixPtr& out);
+  void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
+                        std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
+                        MKLDNNMatrixPtr& out);
+};
+
+}  // namespace paddle

paddle/gserver/layers/MKLDNNConvLayer.cpp

@@ -90,7 +90,7 @@ void MKLDNNConvLayer::convertWeightsToPaddle() {
 }
 
 void MKLDNNConvLayer::reshape(
-    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+    int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
   reshapeInput(bs, ih, iw);
 
   // cal output sizes
@@ -105,21 +105,17 @@ void MKLDNNConvLayer::reshape(
 }
 
 void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
-                               MKLDNNMatrixPtr& in,
-                               MKLDNNMatrixPtr& wgt,
-                               MKLDNNMatrixPtr& bias,
+                               std::vector<MKLDNNMatrixPtr>& inputs,
                                MKLDNNMatrixPtr& out) {
   resetFwdPD(fwdPD_);
 
-  resetFwdBuffers(fwdPD_, in, wgt, bias, out);
+  resetFwdBuffers(fwdPD_, inputs[0], wgtVal_, biasVal_, out);
 
-  resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
+  resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, biasVal_, out);
 }
 
 void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
-                               MKLDNNMatrixPtr& in,
-                               MKLDNNMatrixPtr& wgt,
-                               MKLDNNMatrixPtr& bias,
+                               std::vector<MKLDNNMatrixPtr>& inputs,
                                MKLDNNMatrixPtr& out) {
   std::shared_ptr<conv_bwdWgt::primitive_desc> bwdWgtPD;
   std::shared_ptr<conv_bwdData::primitive_desc> bwdDataPD;
@@ -128,9 +124,10 @@ void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
 
   resetBwdDataPD(bwdDataPD);
 
-  resetBwdBuffers(bwdWgtPD, bwdDataPD, in, wgt, bias, out);
+  resetBwdBuffers(bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
 
-  resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
+  resetBwdPipeline(
+      pipeline, bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
 }
 
 void MKLDNNConvLayer::updateWeights(const UpdateCallback& callback) {
@@ -236,14 +233,14 @@ void MKLDNNConvLayer::resetBwdWgtPD(
   loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
 
   // create backward weight using input, output and weight value memory desc
-  CHECK(inVal_) << "Should have internal input value";
+  CHECK(inVals_[0]) << "Should have internal input value";
   CHECK(outVal_) << "Should have internal output value";
   CHECK(wgtVal_) << "Should have weight value";
   algorithm algo = algorithm::convolution_direct;
   padding_kind padKind = padding_kind::zero;
   auto bwdWgtDesc = biasVal_ != nullptr
                         ? conv_bwdWgt::desc(algo,
-                                            inVal_->getMemoryDesc(),
+                                            inVals_[0]->getMemoryDesc(),
                                             wgtVal_->getMemoryDesc(),
                                             biasVal_->getMemoryDesc(),
                                             outVal_->getMemoryDesc(),
@@ -252,7 +249,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
                                             padR,
                                             padKind)
                         : conv_bwdWgt::desc(algo,
-                                            inVal_->getMemoryDesc(),
+                                            inVals_[0]->getMemoryDesc(),
                                             wgtVal_->getMemoryDesc(),
                                             outVal_->getMemoryDesc(),
                                             strides,
@@ -260,7 +257,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
                                             padR,
                                             padKind);
   pd.reset(new conv_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
-  CHECK_PRIMITIVE_DESC_EQ(inVal_, pd->src_primitive_desc());
+  CHECK_PRIMITIVE_DESC_EQ(inVals_[0], pd->src_primitive_desc());
   CHECK_PRIMITIVE_DESC_EQ(
       outVal_,
       pd->diff_dst_primitive_desc(),
@@ -280,12 +277,12 @@ void MKLDNNConvLayer::resetBwdDataPD(
 
   memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
   loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
-  CHECK(inVal_) << "Should have internal input value";
+  CHECK(inVals_[0]) << "Should have internal input value";
   CHECK(outVal_) << "Should have internal output value";
   // create backward data using input and output value memory desc
   // but using weight memory desc with any format
   auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct,
-                                        inVal_->getMemoryDesc(),
+                                        inVals_[0]->getMemoryDesc(),
                                         MKLDNNMatrix::createMemoryDesc(wgtDims),
                                         outVal_->getMemoryDesc(),
                                         strides,
@@ -294,7 +291,7 @@ void MKLDNNConvLayer::resetBwdDataPD(
                                         padding_kind::zero);
   pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
   CHECK_PRIMITIVE_DESC_EQ(
-      inVal_,
+      inVals_[0],
       pd->diff_src_primitive_desc(),
       "primitive desc of in value and grad should be equal");
   CHECK_PRIMITIVE_DESC_EQ(
@@ -346,12 +343,12 @@ void MKLDNNConvLayer::resetBwdPipeline(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  CHECK(inVal_);
+  CHECK(inVals_[0]);
   // add bwdWgt handle
   if (bias) {
-    bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt, *bias));
+    bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt, *bias));
   } else {
-    bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt));
+    bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt));
   }
   pipeline.push_back(*bwdWgt_);
 

paddle/gserver/layers/MKLDNNConvLayer.h

@@ -69,18 +69,14 @@ public:
             const ParameterMap& parameterMap) override;
 
   void reshape(
-      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
 
   void resetFwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void resetBwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void updateWeights(const UpdateCallback& callback) override;
@@ -107,48 +103,26 @@ protected:
                         mkldnn::memory::dims& padL,
                         mkldnn::memory::dims& padR);
 
-  /**
-   * reset the forward primitive descriptor.
-   */
   void resetFwdPD(std::shared_ptr<conv_fwd::primitive_desc>& pd);
-  /**
-   * reset the MKLDNNMatrix buffers used in forward.
-   */
   void resetFwdBuffers(std::shared_ptr<conv_fwd::primitive_desc>& pd,
                        MKLDNNMatrixPtr& in,
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
-  /**
-   * reset the forward pipeline.
-   */
   void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
                         std::shared_ptr<conv_fwd::primitive_desc>& pd,
                         MKLDNNMatrixPtr& in,
                         MKLDNNMatrixPtr& wgt,
                         MKLDNNMatrixPtr& bias,
                         MKLDNNMatrixPtr& out);
-
-  /**
-   * reset the backward weight primitive descriptor.
-   */
   void resetBwdWgtPD(std::shared_ptr<conv_bwdWgt::primitive_desc>& pd);
-  /**
-   * reset the backward data primitive descriptor.
-   */
   void resetBwdDataPD(std::shared_ptr<conv_bwdData::primitive_desc>& pd);
-  /**
-   * reset the MKLDNNMatrix buffers used in backward.
-   */
   void resetBwdBuffers(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
                        std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
                        MKLDNNMatrixPtr& in,
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& bias,
                        MKLDNNMatrixPtr& out);
-  /**
-   * reset the backward pipeline.
-   */
   void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
                         std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
                         std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,

paddle/gserver/layers/MKLDNNFcLayer.cpp

@@ -74,7 +74,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
 }
 
 void MKLDNNFcLayer::reshape(
-    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+    int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
   reshapeInput(bs, ih, iw);
 
   CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
@@ -87,32 +87,29 @@ void MKLDNNFcLayer::reshape(
 }
 
 void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
-                             MKLDNNMatrixPtr& in,
-                             MKLDNNMatrixPtr& wgt,
-                             MKLDNNMatrixPtr& bias,
+                             std::vector<MKLDNNMatrixPtr>& inputs,
                              MKLDNNMatrixPtr& out) {
-  resetFwdBuffers(in, wgt, bias, out);
+  resetFwdBuffers(inputs[0], wgtVal_, biasVal_, out);
 
-  resetFwdPD(fwdPD_, in, wgt, bias, out);
+  resetFwdPD(fwdPD_, inputs[0], wgtVal_, biasVal_, out);
 
-  resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
+  resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, biasVal_, out);
 }
 
 void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
-                             MKLDNNMatrixPtr& in,
-                             MKLDNNMatrixPtr& wgt,
-                             MKLDNNMatrixPtr& bias,
+                             std::vector<MKLDNNMatrixPtr>& inputs,
                              MKLDNNMatrixPtr& out) {
   std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD;
   std::shared_ptr<fc_bwdData::primitive_desc> bwdDataPD;
 
-  resetBwdBuffers(in, wgt, bias, out);
+  resetBwdBuffers(inputs[0], wgtGrad_, biasGrad_, out);
 
-  resetBwdWgtPD(bwdWgtPD, wgt, bias, out);
+  resetBwdWgtPD(bwdWgtPD, wgtGrad_, biasGrad_, out);
 
-  resetBwdDataPD(bwdDataPD, in, out);
+  resetBwdDataPD(bwdDataPD, inputs[0], out);
 
-  resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
+  resetBwdPipeline(
+      pipeline, bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
 }
 
 void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
@@ -193,9 +190,9 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
                                     MKLDNNMatrixPtr& wgt,
                                     MKLDNNMatrixPtr& bias,
                                     MKLDNNMatrixPtr& out) {
-  CHECK(inVal_ && outVal_);
+  CHECK(inVals_[0] && outVal_);
   resetOutGrad(out, outVal_->getPrimitiveDesc());
-  resetInGrad(in, inVal_->getPrimitiveDesc());
+  resetInGrad(in, inVals_[0]->getPrimitiveDesc());
 
   CHECK(wgtVal_);
   resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
@@ -212,14 +209,15 @@ void MKLDNNFcLayer::resetBwdWgtPD(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  CHECK(inVal_);
-  fc_bwdWgt::desc bwdWgtDesc = bias ? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
-                                                      wgt->getMemoryDesc(),
-                                                      bias->getMemoryDesc(),
-                                                      out->getMemoryDesc())
-                                    : fc_bwdWgt::desc(inVal_->getMemoryDesc(),
-                                                      wgt->getMemoryDesc(),
-                                                      out->getMemoryDesc());
+  CHECK(inVals_[0]);
+  fc_bwdWgt::desc bwdWgtDesc =
+      bias ? fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
+                             wgt->getMemoryDesc(),
+                             bias->getMemoryDesc(),
+                             out->getMemoryDesc())
+           : fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
+                             wgt->getMemoryDesc(),
+                             out->getMemoryDesc());
   pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
 }
 
@@ -245,11 +243,11 @@ void MKLDNNFcLayer::resetBwdPipeline(
     MKLDNNMatrixPtr& wgt,
     MKLDNNMatrixPtr& bias,
     MKLDNNMatrixPtr& out) {
-  CHECK(inVal_);
+  CHECK(inVals_[0]);
   if (bias) {
-    bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));
+    bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt, *bias));
   } else {
-    bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt));
+    bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt));
   }
   pipeline.push_back(*bwdWgt_);
 

paddle/gserver/layers/MKLDNNFcLayer.h

@@ -52,18 +52,14 @@ public:
             const ParameterMap& parameterMap) override;
 
   void reshape(
-      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
 
   void resetFwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void resetBwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void updateWeights(const UpdateCallback& callback) override;
@@ -73,11 +69,6 @@ public:
   void convertWeightsToPaddle() override;
 
 protected:
-  /**
-   * Forward functions: reset buffers(input, output, weight and bias),
-   *                    reset primitive descriptor,
-   *                    reset pipeline.
-   */
   void resetFwdBuffers(MKLDNNMatrixPtr& in,
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& bias,
@@ -93,13 +84,6 @@ protected:
                         MKLDNNMatrixPtr& wgt,
                         MKLDNNMatrixPtr& bias,
                         MKLDNNMatrixPtr& out);
-
-  /**
-   * Backward functions: reset buffers(input, output, weight and bias),
-   *                     reset primitive descriptor for backward weight,
-   *                     reset primitive descriptor for backward data,
-   *                     reset pipeline.
-   */
   void resetBwdBuffers(MKLDNNMatrixPtr& in,
                        MKLDNNMatrixPtr& wgt,
                        MKLDNNMatrixPtr& bias,

paddle/gserver/layers/MKLDNNLayer.cpp

@@ -21,8 +21,8 @@ namespace paddle {
 
 bool MKLDNNLayer::init(const LayerMap& layerMap,
                        const ParameterMap& parameterMap) {
-  CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
-                          << "Please set WITH_MKLDNN=ON "
+  CHECK(FLAGS_use_mkldnn) << "MKLDNNLayers only support use_mkldnn."
+                          << "Please set WITH_MKL=ON "
                           << "and set use_mkldnn=True";
   CHECK(!useGpu_) << "Do not support GPU yet";
 
@@ -48,31 +48,20 @@ void MKLDNNLayer::forward(PassType passType) {
     REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
     CHECK(!inputLayers_.empty());
     copySeqInfoToOutputs();
-    size_t elemenCnt = inputLayers_[0]->getOutputValue()->getElementCnt();
-    if (inputElemenCnt_ != elemenCnt) {
+    if (condition_ != keepCondition()) {
       VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
-      // reset when input total sizes changed, not only the batchsize
-      inputElemenCnt_ = elemenCnt;
-      pipelineFwd_.clear();
+      condition_ = keepCondition();
       reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
-      // all cpu device output grad or value share output's
+      printSizeInfo();
+      // the output_.value and output_.grad are shared with CPU device
       shareCPUDevice();
-      resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
-      // MKLDNNLayer output value should be MKLDNNMatrix
-      // so external output value is necessary.
-      // Then external input value is not necessary,
-      // since input may be mkldnn internal buffer.
-      CHECK(extOutVal_) << "external output value is necessary";
-      output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
-      CHECK(inVal_ && outVal_) << "internal memories are necessary";
-      if (cvtInVal_) {
-        pipelineFwd_.insert(pipelineFwd_.begin(), *cvtInVal_);
-      }
-      if (cvtOutVal_) {
-        pipelineFwd_.push_back(*cvtOutVal_);
-      }
+      pipelineFwd_.clear();
+      inVals_.resize(inputLayers_.size(), nullptr);
+      extInVals_.resize(inputLayers_.size(), nullptr);
+      cvtInVals_.resize(inputLayers_.size(), nullptr);
+      resetFwd(pipelineFwd_, inVals_, outVal_);
+      prepareValueConversions(pipelineFwd_);
       convertWeightsFromPaddle();
-      printSizeInfo();
       printValueFormat();
       needResetBwd_ = true;
     }
@@ -80,8 +69,8 @@ void MKLDNNLayer::forward(PassType passType) {
     if (inputLayers_[0]->getType() == "data" && inputLayers_.size() == 1) {
       // Update input value data when input layer is "data" type,
       // since the input value data address might be changed.
-      CHECK(extInVal_);
-      extInVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
+      CHECK(extInVals_[0]);
+      extInVals_[0]->setData(getInputValue(0, CPU_DEVICE)->getData());
     }
 
     if (!outputOnlyMKLDNN_) {
@@ -99,22 +88,13 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
   if (needResetBwd_) {
     VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
     pipelineBwd_.clear();
+    inGrads_.resize(inputLayers_.size(), nullptr);
+    extInGrads_.resize(inputLayers_.size(), nullptr);
+    cvtInGrads_.resize(inputLayers_.size(), nullptr);
     pipelineMergeGrad_.clear();
     mergeGrad_ = nullptr;
-    resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
-    // external output grad is not necessary
-    // since output may be mkldnn internal buffer or merge them directly.
-    CHECK(outGrad_) << "internal output grad is necessary";
-    if (extOutGrad_) {
-      CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
-          << "the external buffer should share the same data with output_.grad";
-    }
-    if (cvtOutGrad_) {
-      pipelineBwd_.insert(pipelineBwd_.begin(), *cvtOutGrad_);
-    }
-    if (cvtInGrad_) {
-      pipelineBwd_.push_back(*cvtInGrad_);
-    }
+    resetBwd(pipelineBwd_, inGrads_, outGrad_);
+    prepareGradConversions(pipelineBwd_);
     printGradFormat();
     needResetBwd_ = false;
   }
@@ -138,8 +118,11 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
   }
 }
 
-void MKLDNNLayer::reshapeInput(int& batchsize, int& height, int& width) {
-  const Argument& input = inputLayers_[0]->getOutput();
+void MKLDNNLayer::reshapeInput(int& batchsize,
+                               int& height,
+                               int& width,
+                               size_t idx) {
+  const Argument& input = inputLayers_[idx]->getOutput();
   batchsize = input.getBatchSize();
   int h = input.getFrameHeight();
   int w = input.getFrameWidth();
@@ -173,27 +156,30 @@ void MKLDNNLayer::resetWithMatrix(MKLDNNMatrixPtr& dnn,
 void MKLDNNLayer::resetInValue(
     MKLDNNMatrixPtr& in,
     const std::shared_ptr<memory::primitive_desc>& intPD,
-    size_t inputIdx) {
-  cvtInVal_ = nullptr;
-  extInVal_ = nullptr;
+    size_t idx,
+    int inputChannel) {
+  cvtInVals_[idx] = nullptr;
+  extInVals_[idx] = nullptr;
   in = nullptr;
-  CHECK_GT(bs_ * ic_ * ih_ * iw_, 0);
+  inputChannel = inputChannel == 0 ? ic_ : inputChannel;
+  CHECK_GT(bs_ * inputChannel * ih_ * iw_, 0);
   auto extPD = MKLDNNMatrix::createPrimitiveDesc(
-      {bs_, ic_, ih_, iw_}, format::nchw, engine_);
-  const MatrixPtr& inMat = inputLayers_[inputIdx]->getOutputValue();
-  extInVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
-  CHECK_EQ(inputIsOnlyMKLDNN(), extInVal_ != nullptr);
-  if (extInVal_ == nullptr || extInVal_->getFormat() == format::nc) {
-    extInVal_ = MKLDNNMatrix::create(extPD, inMat);
+      {bs_, inputChannel, ih_, iw_}, format::nchw, engine_);
+  const MatrixPtr& inMat = inputLayers_[idx]->getOutputValue();
+  extInVals_[idx] = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
+  CHECK_EQ(inputIsOnlyMKLDNN(), extInVals_[idx] != nullptr);
+  if (extInVals_[idx] == nullptr ||
+      extInVals_[idx]->getFormat() == format::nc) {
+    extInVals_[idx] = MKLDNNMatrix::create(extPD, inMat);
   }
-  in = extInVal_;
+  in = extInVals_[idx];
   if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
     return;
   }
   // need create reorder
   in = MKLDNNMatrix::create(*intPD);
-  cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in);
-  CHECK(cvtInVal_) << "should not be emptry";
+  cvtInVals_[idx] = MKLDNNMatrix::createReorder(extInVals_[idx], in);
+  CHECK(cvtInVals_[idx]) << "should not be emptry";
 }
 
 void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
@@ -215,11 +201,11 @@ void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
 
 void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
                               memory::primitive_desc intPD,
-                              size_t inputIdx) {
-  cvtInGrad_ = nullptr;
-  extInGrad_ = nullptr;
+                              size_t idx) {
+  cvtInGrads_[idx] = nullptr;
+  extInGrads_[idx] = nullptr;
   in = nullptr;
-  LayerPtr& input = inputLayers_[inputIdx];
+  LayerPtr& input = inputLayers_[idx];
   if (input->getOutputGrad() == nullptr) {
     // no need input grad
     return;
@@ -234,23 +220,25 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
   in = MKLDNNMatrix::create(intPD, inMat);
   Argument& arg = input->getOutput(this->getName());
   arg.grad = std::dynamic_pointer_cast<Matrix>(in);
-  CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
+  CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
   if (inputIsOnlyMKLDNN()) {
     return;
   }
 
-  extInGrad_ = in;
-  if (isPaddleFormat(extInGrad_->getFormat())) {
+  extInGrads_[idx] = in;
+  if (isPaddleFormat(extInGrads_[idx]->getFormat())) {
     return;
   }
   // need create reorder
-  CHECK(extInVal_ != nullptr && isPaddleFormat(extInVal_->getFormat()))
+  CHECK(extInVals_[idx] != nullptr &&
+        isPaddleFormat(extInVals_[idx]->getFormat()))
       << "should have external input value and the format must be nchw(nc)";
-  extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat);
-  CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
+  extInGrads_[idx] =
+      MKLDNNMatrix::create(extInVals_[idx]->getPrimitiveDesc(), inMat);
+  CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
   in = MKLDNNMatrix::create(intPD);
-  cvtInGrad_ = MKLDNNMatrix::createReorder(in, extInGrad_);
-  CHECK(cvtInGrad_);
+  cvtInGrads_[idx] = MKLDNNMatrix::createReorder(in, extInGrads_[idx]);
+  CHECK(cvtInGrads_[idx]);
 }
 
 void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out,

paddle/gserver/layers/MKLDNNLayer.h

@@ -34,15 +34,16 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
  */
 class MKLDNNLayer : public Layer {
 protected:
-  // input value element count
-  size_t inputElemenCnt_;
   // batch size
   int bs_;
+  // their sizes are always from the first input layer
   // input image channel, height and width
   int ic_, ih_, iw_;
   // output image channel, height and width
   int oc_, oh_, ow_;
 
+  // the condition that forward need be reset
+  size_t condition_;
   // backward also need reset after reset forward handle
   bool needResetBwd_;
 
@@ -67,18 +68,18 @@ protected:
    * When all layers are mkldnn layers, they could save internal data.
    */
   // below MKLDNNMatrix buffers are all internal buffers
-  MKLDNNMatrixPtr inVal_;
-  MKLDNNMatrixPtr inGrad_;
+  std::vector<MKLDNNMatrixPtr> inVals_;
+  std::vector<MKLDNNMatrixPtr> inGrads_;
   MKLDNNMatrixPtr outVal_;
   MKLDNNMatrixPtr outGrad_;
   // below are external value and grad
-  MKLDNNMatrixPtr extInVal_;
-  MKLDNNMatrixPtr extInGrad_;
+  std::vector<MKLDNNMatrixPtr> extInVals_;
+  std::vector<MKLDNNMatrixPtr> extInGrads_;
   MKLDNNMatrixPtr extOutVal_;
   MKLDNNMatrixPtr extOutGrad_;
   // convert handle between external and internal buffers
-  std::shared_ptr<mkldnn::reorder> cvtInVal_;
-  std::shared_ptr<mkldnn::reorder> cvtInGrad_;
+  std::vector<std::shared_ptr<mkldnn::reorder>> cvtInVals_;
+  std::vector<std::shared_ptr<mkldnn::reorder>> cvtInGrads_;
   std::shared_ptr<mkldnn::reorder> cvtOutVal_;
   std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
 
@@ -102,14 +103,7 @@ protected:
 public:
   explicit MKLDNNLayer(const LayerConfig& config)
       : Layer(config),
-        inputElemenCnt_(0),
-        bs_(0),
-        ic_(0),
-        ih_(0),
-        iw_(0),
-        oc_(0),
-        oh_(0),
-        ow_(0),
+        condition_(0),
         needResetBwd_(true),
         outputOnlyMKLDNN_(false),
         engine_(mkldnn::engine::cpu, 0),
@@ -125,31 +119,28 @@ public:
   virtual void backward(const UpdateCallback& callback);
 
   /**
-   * reshape the input image sizes
-   * and reset output image and buffer size
-   * output channel can not be changed
+   * reshape the input and output channels and image sizes
+   * and reset output buffer size
    */
   virtual void reshape(
-      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) = 0;
 
   /**
    * reset the mkldnn forward primitve and memories
    * only would be called when input size changes
+   * weight and bias buffers should be coverd by child class itself
    */
   virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
-                        MKLDNNMatrixPtr& in,
-                        MKLDNNMatrixPtr& wgt,
-                        MKLDNNMatrixPtr& bias,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
                         MKLDNNMatrixPtr& out) = 0;
 
   /**
    * reset the mkldnn backward primitve and memories
    * only would be called when needed
+   * weight and bias buffers should be coverd by child class itself
    */
   virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
-                        MKLDNNMatrixPtr& in,
-                        MKLDNNMatrixPtr& wgt,
-                        MKLDNNMatrixPtr& bias,
+                        std::vector<MKLDNNMatrixPtr>& inputs,
                         MKLDNNMatrixPtr& out) = 0;
 
   /**
@@ -175,10 +166,19 @@ public:
   void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
 
 protected:
+  /**
+   * Some layers may have different condition to reset the forward.
+   * The function returns the condition that do not need reset forward.
+   */
+  inline virtual size_t keepCondition() {
+    // reset when the first input element size changed, not only the batchsize
+    return inputLayers_[0]->getOutputValue()->getElementCnt();
+  }
+
   /**
    * reshape the input image sizes and input batchsize
    */
-  void reshapeInput(int& batchsize, int& height, int& width);
+  void reshapeInput(int& batchsize, int& height, int& width, size_t idx = 0);
 
   /**
    * reshape output image sizes
@@ -196,11 +196,13 @@ protected:
   /**
    * reset input value from input MKLDNNMatrix and internal primitive desc.
    * reset both internal and external buffer and create reorder if necessary.
+   * input channel may be different in concat.
    */
   void resetInValue(
       MKLDNNMatrixPtr& in,
       const std::shared_ptr<mkldnn::memory::primitive_desc>& intPD = nullptr,
-      size_t inputIdx = 0);
+      size_t idx = 0,
+      int inputChannel = 0);
 
   /**
    * reset output value from internal primitive desc.
@@ -215,7 +217,7 @@ protected:
    */
   void resetInGrad(MKLDNNMatrixPtr& in,
                    mkldnn::memory::primitive_desc intPD,
-                   size_t inputIdx = 0);
+                   size_t idx = 0);
 
   /**
    * reset output grad from internal primitive desc.
@@ -293,17 +295,19 @@ protected:
    * print the mkldnn memory format of value
    */
   virtual void printValueFormat() {
-    if (extInVal_) {
-      VLOG(MKLDNN_FMTS) << extInVal_->getFormat() << " >>> ";
-    }
-    if (inVal_) {
-      VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>>";
+    for (size_t i = 0; i < inVals_.size(); ++i) {
+      if (!inVals_[i]) {
+        continue;
+      }
+      VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
+                        << ": " << (extInVals_[i] ? extInVals_[i]->getFormat()
+                                                  : inVals_[i]->getFormat())
+                        << " >>> " << inVals_[i]->getFormat() << " >>>";
     }
     if (outVal_) {
-      VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
-    }
-    if (extOutVal_) {
-      VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
+      VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> "
+                        << (extOutVal_ ? extOutVal_->getFormat()
+                                       : outVal_->getFormat());
     }
     if (wgtVal_) {
       VLOG(MKLDNN_FMTS) << "Weight value format: " << wgtVal_->getFormat();
@@ -317,17 +321,19 @@ protected:
    * print the mkldnn memory format of grad
    */
   virtual void printGradFormat() {
-    if (extOutGrad_) {
-      VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
-    }
     if (outGrad_) {
-      VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
+      VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< "
+                        << (extOutGrad_ ? extOutGrad_->getFormat()
+                                        : outGrad_->getFormat());
     }
-    if (inGrad_) {
-      VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<";
-    }
-    if (extInGrad_) {
-      VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< ";
+    for (size_t i = 0; i < inGrads_.size(); ++i) {
+      if (!inGrads_[i]) {
+        continue;
+      }
+      VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
+                        << ": " << (extInGrads_[i] ? extInGrads_[i]->getFormat()
+                                                   : inGrads_[i]->getFormat())
+                        << " <<< " << inGrads_[i]->getFormat() << " <<<";
     }
     if (wgtGrad_) {
       VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat();
@@ -434,6 +440,41 @@ private:
       outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
     }
   }
+
+  void prepareValueConversions(std::vector<mkldnn::primitive>& pipeline) {
+    // MKLDNNLayer output value should be MKLDNNMatrix
+    // so external output value is necessary.
+    // Then external input value is not necessary,
+    // since input may be mkldnn internal buffer.
+    CHECK(extOutVal_) << "external output value is necessary";
+    output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
+    CHECK(inVals_[0] && outVal_) << "internal memories are necessary";
+    for (size_t i = 0; i < cvtInVals_.size(); ++i) {
+      if (cvtInVals_[i]) {
+        pipeline.insert(pipeline.begin(), *cvtInVals_[i]);
+      }
+    }
+    if (cvtOutVal_) {
+      pipeline.push_back(*cvtOutVal_);
+    }
+  }
+  void prepareGradConversions(std::vector<mkldnn::primitive>& pipeline) {
+    // external output grad is not necessary
+    // since output may be mkldnn internal buffer or merge them directly.
+    CHECK(outGrad_) << "internal output grad is necessary";
+    if (extOutGrad_) {
+      CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
+          << "the external buffer should share the same data with output_.grad";
+    }
+    if (cvtOutGrad_) {
+      pipeline.insert(pipeline.begin(), *cvtOutGrad_);
+    }
+    for (size_t i = 0; i < cvtInGrads_.size(); ++i) {
+      if (cvtInGrads_[i]) {
+        pipeline.push_back(*cvtInGrads_[i]);
+      }
+    }
+  }
 };
 
 }  // namespace paddle

paddle/gserver/layers/MKLDNNPoolLayer.cpp

@@ -58,10 +58,11 @@ bool MKLDNNPoolLayer::init(const LayerMap& layerMap,
 }
 
 void MKLDNNPoolLayer::reshape(
-    int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+    int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
   reshapeInput(bs, ih, iw);
   // ic_ and oc can not be changed
-  CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
+  CHECK_EQ((size_t)ic,
+           inputLayers_[0]->getOutputValue()->getElementCnt() / bs / ih / iw)
       << "Input channel can not be changed";
 
   // cal output sizes
@@ -74,29 +75,25 @@ void MKLDNNPoolLayer::reshape(
 }
 
 void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
-                               MKLDNNMatrixPtr& in,
-                               MKLDNNMatrixPtr& wgt,
-                               MKLDNNMatrixPtr& bias,
+                               std::vector<MKLDNNMatrixPtr>& inputs,
                                MKLDNNMatrixPtr& out) {
-  resetFwdBuffers(in, out);
+  resetFwdBuffers(inputs[0], out);
 
-  resetFwdPD(fwdPD_, in, out);
+  resetFwdPD(fwdPD_, inputs[0], out);
 
-  resetFwdPipeline(pipeline, fwdPD_, in, out);
+  resetFwdPipeline(pipeline, fwdPD_, inputs[0], out);
 }
 
 void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
-                               MKLDNNMatrixPtr& in,
-                               MKLDNNMatrixPtr& wgt,
-                               MKLDNNMatrixPtr& bias,
+                               std::vector<MKLDNNMatrixPtr>& inputs,
                                MKLDNNMatrixPtr& out) {
   std::shared_ptr<pool_bwd::primitive_desc> pd;
 
-  resetBwdBuffers(in, out);
+  resetBwdBuffers(inputs[0], out);
 
-  resetBwdPD(pd, in, out);
+  resetBwdPD(pd, inputs[0], out);
 
-  resetBwdPipeline(pipeline, pd, in, out);
+  resetBwdPipeline(pipeline, pd, inputs[0], out);
 }
 
 void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
@@ -151,9 +148,9 @@ void MKLDNNPoolLayer::resetFwdPipeline(
 
 void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
                                       MKLDNNMatrixPtr& out) {
-  CHECK(inVal_ && outVal_);
+  CHECK(inVals_[0] && outVal_);
   resetOutGrad(out, outVal_->getPrimitiveDesc());
-  resetInGrad(in, inVal_->getPrimitiveDesc());
+  resetInGrad(in, inVals_[0]->getPrimitiveDesc());
 }
 
 void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,

paddle/gserver/layers/MKLDNNPoolLayer.h

@@ -53,18 +53,14 @@ public:
             const ParameterMap& parameterMap) override;
 
   void reshape(
-      int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
+      int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
 
   void resetFwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void resetBwd(std::vector<mkldnn::primitive>& pipeline,
-                MKLDNNMatrixPtr& in,
-                MKLDNNMatrixPtr& wgt,
-                MKLDNNMatrixPtr& bias,
+                std::vector<MKLDNNMatrixPtr>& inputs,
                 MKLDNNMatrixPtr& out) override;
 
   void printSizeInfo() override {
@@ -75,11 +71,6 @@ public:
   }
 
 protected:
-  /**
-   * Forward functions: reset buffers(input, output),
-   *                    reset primitive descriptor,
-   *                    reset pipeline.
-   */
   void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
   void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
                   MKLDNNMatrixPtr in,
@@ -88,12 +79,6 @@ protected:
                         std::shared_ptr<pool_fwd::primitive_desc>& pd,
                         MKLDNNMatrixPtr& in,
                         MKLDNNMatrixPtr& out);
-
-  /**
-   * Backward functions: reset buffers(input, output),
-   *                     reset primitive descriptor,
-   *                     reset pipeline.
-   */
   void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
   void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
                   MKLDNNMatrixPtr& in,

paddle/gserver/tests/CMakeLists.txt

@@ -29,7 +29,7 @@ gserver_test(test_KmaxSeqScore)
 gserver_test(test_Expand)
 gserver_test(test_MaxPoolingWithMaskOutput)
 
-########## test_Mkldnn layers and activations ##########
+########## test_MKLDNN layers and activations ##########
 if(WITH_MKLDNN)
     add_unittest_without_exec(test_MKLDNN
         test_MKLDNN.cpp
@@ -62,17 +62,6 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
 endif()
 
 if(NOT MOBILE_INFERENCE)
-################### test_ProtoDataProvider ############
-    add_unittest_without_exec(test_ProtoDataProvider
-        test_ProtoDataProvider.cpp)
-
-    # test_ProtoDataProvider will mkdir as same name,
-    # so if WORKING_DIRECTORY is default directory, then
-    # mkdir will get error.
-    add_test(NAME test_ProtoDataProvider
-        COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider
-        WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
-
 ################## test_Evaluator #######################
     add_unittest(test_Evaluator
         test_Evaluator.cpp)
@@ -110,3 +99,24 @@ add_test(NAME test_PyDataProvider2
    COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
         WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
 )
+
+################# test_CompareSparse ##################
+add_unittest_without_exec(test_CompareSparse
+    test_CompareSparse.cpp)
+if(NOT ON_TRAVIS)
+  add_test(NAME test_CompareSparse
+    COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+          ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+              ./.set_port.sh -p port -n 6
+                  ${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
+endif()
+
+################ test_CompareTwoNets ######################
+add_unittest_without_exec(test_CompareTwoNets
+    test_CompareTwoNets.cpp)
+add_test(NAME test_CompareTwoNets
+  COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
+        ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
+        ${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
+    WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)

paddle/gserver/tests/MKLDNNTester.h

@@ -23,7 +23,7 @@ limitations under the License. */
 namespace paddle {
 
 /**
- * @brief test the functionality of Mkldnnlayers
+ * @brief test the functionality of MKLDNNlayers and MKLDNNActivations
  * refer to paddle original function
  */
 class MKLDNNTester {

paddle/gserver/tests/proto_files.txt

-./test_ProtoDataProvider/data1.bin
-./test_ProtoDataProvider/data2.bin

paddle/gserver/tests/proto_files_compressed.txt

-./test_ProtoDataProvider/data1.bin.gz
-./test_ProtoDataProvider/data2.bin.gz

paddle/trainer/tests/sample_trainer_config_opt_a.conf → paddle/gserver/tests/sequence_lstm.conf

+#!/usr/bin/env python
 # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +15,50 @@
 
 from paddle.trainer_config_helpers import *
 
-################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
 
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
 
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 256
+label_dim = 3
+sparse_update = get_config_arg("sparse_update", bool, False)
 
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
+data = data_layer(name="word", size=dict_dim)
 
-lbl = data_layer(name ="label", size=1)
+emb = embedding_layer(
+    input=data,
+    size=word_dim,
+    param_attr=ParamAttr(sparse_update=sparse_update))
 
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
+with mixed_layer(size=hidden_dim * 4) as lstm_input:
+    lstm_input += full_matrix_projection(input=emb)
+
+lstm = lstmemory(
+    input=lstm_input,
+    act=TanhActivation(),
+    gate_act=SigmoidActivation(),
+    state_act=TanhActivation())
+
+lstm_last = last_seq(input=lstm)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=lstm_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/sample_trainer_config_opt_b.conf → paddle/gserver/tests/sequence_recurrent.py

+#!/usr/bin/env python
 # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,27 +15,42 @@
 
 from paddle.trainer_config_helpers import *
 
-################################### Data Configuration ###################################
-TrainData(ProtoData(files = "trainer/tests/mnist.list"))
-################################### Algorithm Configuration ###################################
-settings(batch_size = 1000,
-         learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
-################################### Network Configuration ###################################
-data = data_layer(name ="input", size=784)
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
 
-fc1 = fc_layer(input=data, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
 
-fc2 = fc_layer(input=fc1, size=800,
-               bias_attr=True,
-               act=SigmoidActivation())
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
 
-output = fc_layer(input=[fc1, fc2], size=10,
-                  bias_attr=True,
-                  act=SoftmaxActivation())
+# This config is designed to be equivalent with sequence_recurrent_group.py
 
-lbl = data_layer(name ="label", size=1)
+data = data_layer(name="word", size=dict_dim)
 
-cost = classification_cost(input=output, label=lbl)
-outputs(cost)
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/gserver/tests/sequence_recurrent_group.py

+#!/usr/bin/env python
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers import *
+
+######################## data source ################################
+dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
+dict_file = dict()
+for line_count, line in enumerate(open(dict_path, "r")):
+    dict_file[line.strip()] = line_count
+
+define_py_data_sources2(
+    train_list='gserver/tests/Sequence/train.list',
+    test_list=None,
+    module='sequenceGen',
+    obj='process',
+    args={"dict_file": dict_file})
+
+settings(batch_size=5)
+######################## network configure ################################
+dict_dim = len(open(dict_path, 'r').readlines())
+word_dim = 128
+hidden_dim = 128
+label_dim = 3
+
+# This config is designed to be equivalent with sequence_recurrent.py
+
+data = data_layer(name="word", size=dict_dim)
+
+emb = embedding_layer(
+    input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
+
+
+def step(y):
+    mem = memory(name="rnn_state", size=hidden_dim)
+    with mixed_layer(
+            name="rnn_state",
+            size=hidden_dim,
+            bias_attr=False,
+            act=SoftmaxActivation()) as out:
+        out += identity_projection(input=y)
+        out += full_matrix_projection(
+            input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
+    return out
+
+
+recurrent = recurrent_group(name="rnn", step=step, input=emb)
+
+recurrent_last = last_seq(input=recurrent)
+
+with mixed_layer(
+        size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
+    output += full_matrix_projection(input=recurrent_last)
+
+outputs(
+    classification_cost(
+        input=output, label=data_layer(
+            name="label", size=1)))

paddle/trainer/tests/test_CompareSparse.cpp → paddle/gserver/tests/test_CompareSparse.cpp

@@ -22,8 +22,7 @@ limitations under the License. */
 using namespace paddle;  // NOLINT
 using namespace std;     // NOLINT
 
-static const string& configFile1 =
-    "trainer/tests/sample_trainer_config_compare_sparse.conf";
+static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
 
 DECLARE_bool(use_gpu);
 DECLARE_string(config);

paddle/trainer/tests/test_CompareTwoNets.cpp → paddle/gserver/tests/test_CompareTwoNets.cpp

@@ -30,8 +30,6 @@ DECLARE_bool(use_gpu);
 DECLARE_string(config);
 DECLARE_string(nics);
 
-DEFINE_string(config_file_a, "", "config of one network to compare");
-DEFINE_string(config_file_b, "", "config of another network to compare");
 DEFINE_bool(need_high_accuracy,
             false,
             "whether need to run in double accuracy");
@@ -42,6 +40,10 @@ DEFINE_double(
 DECLARE_bool(thread_local_rand_use_global_seed);
 DECLARE_int32(seed);
 
+static const string& config_file_a = "gserver/tests/sequence_recurrent.py";
+static const string& config_file_b =
+    "gserver/tests/sequence_recurrent_group.py";
+
 struct ComData {
   vector<Argument> outArgs;
   vector<ParameterPtr> parameters;
@@ -66,6 +68,7 @@ void calcGradient(ComData& data, const string configFile) {
   DataBatch dataBatch;
   int32_t batchSize = trainer.getConfig().opt_config().batch_size();
 
+  trainer.getDataProvider()->reset();
   trainer.getDataProvider()->setSkipShuffle();
   trainer.getDataProvider()->getNextBatch(batchSize, &dataBatch);
 
@@ -167,11 +170,11 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
 
 TEST(Trainer, create) {
   ComData dataA;
-  calcGradient(dataA, FLAGS_config_file_a);
+  calcGradient(dataA, config_file_a);
   LOG(INFO) << "\n\nforwardBackward of Network A is finished\n\n";
 
   ComData dataB;
-  calcGradient(dataB, FLAGS_config_file_b);
+  calcGradient(dataB, config_file_b);
   LOG(INFO) << "\n\nforwardBackward of the Network B is finished\n\n";
 
   compareGradient(dataA, dataB);

paddle/gserver/tests/test_LayerGrad.cpp

@@ -583,6 +583,7 @@ TEST(Layer, maxoutLayer) {
     testLayerGrad(config, "maxout", 10, false, useGpu);
   }
 }
+
 void testFcLayer(string format, size_t nnz) {
   TestConfig config;
   config.biasSize = 1024;
@@ -1081,6 +1082,21 @@ TEST(Layer, InterpolationLayer) {
   }
 }
 
+TEST(Layer, DotProdLayer) {
+  TestConfig config;
+  config.layerConfig.set_type("dot_prod");
+  config.layerConfig.set_size(1);
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
+  config.layerConfig.add_inputs();
+  config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "dot_prod", 10, false, useGpu);
+  }
+}
+
 TEST(Layer, OuterProdLayer) {
   TestConfig config;
   config.layerConfig.set_type("out_prod");
@@ -2429,6 +2445,25 @@ TEST(Layer, ScaleSubRegionLayer) {
   }
 }
 
+TEST(Layer, L2DistanceLayer) {
+  TestConfig config;
+  config.layerConfig.set_type("l2_distance");
+  config.layerConfig.set_size(1);
+  config.biasSize = 0;
+
+  const size_t input_dim = 27;
+  const size_t batch_size = 11;
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", input_dim, 0});
+  config.inputDefs.push_back({INPUT_DATA, "layer_1", input_dim, 0});
+  config.layerConfig.add_inputs();
+  config.layerConfig.add_inputs();
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "l2_distance", batch_size, false, useGpu);
+  }
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);

paddle/gserver/tests/test_MKLDNN.cpp

@@ -313,6 +313,47 @@ TEST(MKLDNNLayer, AddtoLayer) {
   testAddtoLayer({4, 12, 1, 1}, 3);
 }
 
+static void getMKLDNNConcatConfig(TestConfig& cfg,
+                                  const std::vector<testImageDesc>& inputs) {
+  CHECK_GE(inputs.size(), 2UL) << "at least two inputs";
+  int oc = inputs[0].ic;
+  for (size_t i = 1; i < inputs.size(); ++i) {
+    CHECK_EQ(inputs[i].bs, inputs[0].bs);
+    CHECK_EQ(inputs[i].ih, inputs[0].ih);
+    CHECK_EQ(inputs[i].iw, inputs[0].iw);
+    oc += inputs[i].ic;
+  }
+  cfg.biasSize = 0;
+  cfg.layerConfig.set_type("mkldnn_concat");
+  cfg.layerConfig.set_size(oc * inputs[0].ih * inputs[0].iw);
+  cfg.layerConfig.set_active_type("relu");
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    std::stringstream ss;
+    ss << "layer_" << i;
+    cfg.inputDefs.push_back(
+        {INPUT_DATA,
+         ss.str(),
+         (size_t)(inputs[i].ic) * inputs[i].ih * inputs[i].iw,
+         0});
+    LayerInputConfig* input = cfg.layerConfig.add_inputs();
+    ImageConfig* img_conf = input->mutable_image_conf();
+    img_conf->set_channels(inputs[i].ic);
+    img_conf->set_img_size_y(inputs[i].ih);
+    img_conf->set_img_size(inputs[i].iw);
+  }
+}
+
+void testConcatLayer(const std::vector<testImageDesc>& inputs) {
+  TestConfig dnnConfig;
+  getMKLDNNConcatConfig(dnnConfig, inputs);
+  RUN_MKLDNN_TEST_LAYER(dnnConfig, "concat", inputs[0])
+}
+
+TEST(MKLDNNLayer, ConcatLayer) {
+  testConcatLayer({{64, 128, 1, 1}, {64, 32, 1, 1}, {64, 64, 1, 1}});
+  testConcatLayer({{32, 100, 8, 8}, {32, 10, 8, 8}});
+}
+
 void testActivation(std::string actType, const testImageDesc& pm) {
   // TODO(TJ): remove me when paddle support elu activation
   if (actType == "mkldnn_elu") {

paddle/operators/CMakeLists.txt

@@ -61,6 +61,18 @@ function(op_library TARGET)
         set(pybind_flag 1)
     endif()
 
+    if ("${TARGET}" STREQUAL "compare_op")
+        set(pybind_flag 1)
+        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+    endif()
+
+    # conv_op contains several operators
+    if ("${TARGET}" STREQUAL "conv_op")
+        set(pybind_flag 1)
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
+    endif()
+
     # pool_op contains several operators
     if ("${TARGET}" STREQUAL "pool_op")
         set(pybind_flag 1)
@@ -68,23 +80,23 @@ function(op_library TARGET)
         file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
     endif()
 
-    if ("${TARGET}" STREQUAL "compare_op")
+    # pool_cudnn_op contains several operators
+    if ("${TARGET}" STREQUAL "pool_cudnn_op")
         set(pybind_flag 1)
-        file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
+        # It's enough to just adding one operator to pybind
+        file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
     endif()
 
-    # pool_with_index_op contains several operators
-    if ("${TARGET}" STREQUAL "pool_with_index_op")
+    if ("${TARGET}" STREQUAL "logical_op")
         set(pybind_flag 1)
-        # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
+        file(APPEND ${pybind_file} "USE_OP(logical_and);\n")
     endif()
 
-    # conv_op contains several operators
-    if ("${TARGET}" STREQUAL "conv_op")
+    # pool_with_index_op contains several operators
+    if ("${TARGET}" STREQUAL "pool_with_index_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
+        file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
     endif()
 
     # conv_transpose_op contains several operators
@@ -93,12 +105,12 @@ function(op_library TARGET)
         # It's enough to just adding one operator to pybind
         file(APPEND ${pybind_file} "USE_OP(conv2d_transpose);\n")
     endif()
-    
-    # pool_cudnn_op contains several operators
-    if ("${TARGET}" STREQUAL "pool_cudnn_op")
+
+    # conv_transpose_cudnn_op contains two operators
+    if ("${TARGET}" STREQUAL "conv_transpose_cudnn_op")
         set(pybind_flag 1)
         # It's enough to just adding one operator to pybind
-        file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
+        file(APPEND ${pybind_file} "USE_OP(conv2d_transpose_cudnn);\n")
     endif()
 
     # save_restore_op contains several operators
@@ -172,6 +184,7 @@ set(DEPS_OPS
     sequence_softmax_op
     sum_op
     pool_op
+    maxout_op
     pool_with_index_op
     conv_op
     conv_transpose_op
@@ -198,6 +211,7 @@ op_library(sgd_op DEPS selected_rows_functor)
 op_library(adagrad_op DEPS selected_rows_functor)
 op_library(conv_op DEPS vol2col)
 op_library(pool_op DEPS pooling)
+op_library(maxout_op DEPS maxouting)
 op_library(pool_with_index_op DEPS pooling)
 op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
 op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)

paddle/operators/activation_op.cc

@@ -98,7 +98,6 @@ $y = \max(x, 0)$
   }
 };
 
-template <typename AttrType>
 class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   LeakyReluOpMaker(framework::OpProto *proto,
@@ -106,8 +105,7 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of LeakyRelu operator");
     AddOutput("Y", "Output of LeakyRelu operator");
-    AddAttr<AttrType>("alpha", "The small negative slope")
-        .SetDefault(static_cast<AttrType>(0.02f));
+    AddAttr<float>("alpha", "The small negative slope").SetDefault(0.02f);
     AddComment(R"DOC(
 LeakyRelu Activation Operator.
 
@@ -117,7 +115,6 @@ $y = \max(x, \alpha * x)$
   }
 };
 
-template <typename AttrType>
 class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SoftShrinkOpMaker(framework::OpProto *proto,
@@ -125,8 +122,7 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of Softshrink operator");
     AddOutput("Y", "Output of Softshrink operator");
-    AddAttr<AttrType>("lambda", "non-negative offset")
-        .SetDefault(static_cast<AttrType>(0.5f));
+    AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
     AddComment(R"DOC(
 Softshrink Activation Operator.
 
@@ -173,7 +169,6 @@ $$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
   }
 };
 
-template <typename AttrType>
 class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   HardShrinkOpMaker(framework::OpProto *proto,
@@ -181,8 +176,8 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of HardShrink operator");
     AddOutput("Y", "Output of HardShrink operator");
-    AddAttr<AttrType>("threshold", "The value of threshold for HardShrink")
-        .SetDefault(static_cast<AttrType>(0.5));
+    AddAttr<float>("threshold", "The value of threshold for HardShrink")
+        .SetDefault(0.5f);
     AddComment(R"DOC(
 HardShrink Activation Operator.
 
@@ -308,17 +303,16 @@ $$y = \frac{x}{1 + |x|}$$
   }
 };
 
-template <typename AttrType>
 class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   BReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of BRelu operator");
     AddOutput("Y", "Output of BRelu operator");
-    AddAttr<AttrType>("t_min", "The min marginal value of BRelu")
-        .SetDefault(static_cast<AttrType>(0));
-    AddAttr<AttrType>("t_max", "The max marginal value of BRelu")
-        .SetDefault(static_cast<AttrType>(24));
+    AddAttr<float>("t_min", "The min marginal value of BRelu")
+        .SetDefault(static_cast<float>(0));
+    AddAttr<float>("t_max", "The max marginal value of BRelu")
+        .SetDefault(static_cast<float>(24));
     AddComment(R"DOC(
 BRelu Activation Operator.
 
@@ -328,7 +322,6 @@ $y = \max(\min(x, t_{min}), t_{max})$
   }
 };
 
-template <typename AttrType>
 class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SoftReluOpMaker(framework::OpProto *proto,
@@ -336,8 +329,8 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of SoftRelu operator");
     AddOutput("Y", "Output of SoftRelu operator");
-    AddAttr<AttrType>("threshold", "The threshold value of SoftRelu")
-        .SetDefault(static_cast<AttrType>(40));
+    AddAttr<float>("threshold", "The threshold value of SoftRelu")
+        .SetDefault(40.0f);
     AddComment(R"DOC(
 SoftRelu Activation Operator.
 
@@ -347,15 +340,13 @@ $y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
   }
 };
 
-template <typename AttrType>
 class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   ELUOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of ELU operator");
     AddOutput("Y", "Output of ELU operator");
-    AddAttr<AttrType>("alpha", "The alpha value of ELU")
-        .SetDefault(static_cast<AttrType>(1.0f));
+    AddAttr<float>("alpha", "The alpha value of ELU").SetDefault(1.0f);
     AddComment(R"DOC(
 ELU Activation Operator.
 
@@ -368,15 +359,14 @@ $y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
   }
 };
 
-template <typename AttrType>
 class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   Relu6OpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of Relu6 operator");
     AddOutput("Y", "Output of Relu6 operator");
-    AddAttr<AttrType>("threshold", "The threshold value of Relu6")
-        .SetDefault(static_cast<AttrType>(6));
+    AddAttr<float>("threshold", "The threshold value of Relu6")
+        .SetDefault(6.0f);
     AddComment(R"DOC(
 Relu6 Activation Operator.
 
@@ -386,15 +376,13 @@ $y = \min(\max(0, x), 6)$
   }
 };
 
-template <typename AttrType>
 class PowOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   PowOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of Pow operator");
     AddOutput("Y", "Output of Pow operator");
-    AddAttr<AttrType>("factor", "The exponential factor of Pow")
-        .SetDefault(static_cast<AttrType>(1));
+    AddAttr<float>("factor", "The exponential factor of Pow").SetDefault(1.0f);
     AddComment(R"DOC(
 Pow Activation Operator.
 
@@ -404,17 +392,16 @@ $y = x^{factor}$
   }
 };
 
-template <typename AttrType>
 class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   STanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of STanh operator");
     AddOutput("Y", "Output of STanh operator");
-    AddAttr<AttrType>("scale_a", "The scale parameter of a for the input")
-        .SetDefault(static_cast<AttrType>(2 / 3));
-    AddAttr<AttrType>("scale_b", "The scale parameter of b for the input")
-        .SetDefault(static_cast<AttrType>(1.7159));
+    AddAttr<float>("scale_a", "The scale parameter of a for the input")
+        .SetDefault(2.0f / 3.0f);
+    AddAttr<float>("scale_b", "The scale parameter of b for the input")
+        .SetDefault(1.7159f);
     AddComment(R"DOC(
 STanh Activation Operator.
 
@@ -424,7 +411,6 @@ $$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
   }
 };
 
-template <typename AttrType>
 class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   ThresholdedReluOpMaker(framework::OpProto *proto,
@@ -432,8 +418,8 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of ThresholdedRelu operator");
     AddOutput("Y", "Output of ThresholdedRelu operator");
-    AddAttr<AttrType>("threshold", "The threshold location of activation")
-        .SetDefault(static_cast<AttrType>(1.0));
+    AddAttr<float>("threshold", "The threshold location of activation")
+        .SetDefault(1.0f);
     AddComment(R"DOC(
 ThresholdedRelu Activation Operator.
 
@@ -448,7 +434,6 @@ $$
   }
 };
 
-template <typename AttrType>
 class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   HardSigmoidOpMaker(framework::OpProto *proto,
@@ -456,10 +441,10 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "Input of HardSigmoid operator");
     AddOutput("Y", "Output of HardSigmoid operator");
-    AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
-        .SetDefault(static_cast<AttrType>(0.2));
-    AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
-        .SetDefault(static_cast<AttrType>(0.5));
+    AddAttr<float>("slope", "Slope for linear approximation of sigmoid")
+        .SetDefault(0.2f);
+    AddAttr<float>("offset", "Offset for linear approximation of sigmoid")
+        .SetDefault(0.5f);
     AddComment(R"DOC(
 HardSigmoid Activation Operator.
 
@@ -499,7 +484,7 @@ REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad,
 REGISTER_OP(tanh_shrink, ops::ActivationOp, ops::TanhShrinkOpMaker,
             tanh_shrink_grad, ops::ActivationOpGrad);
 
-REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker<float>,
+REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker,
             softshrink_grad, ops::ActivationOpGrad);
 
 REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad,
@@ -523,35 +508,34 @@ REGISTER_OP(softplus, ops::ActivationOp, ops::SoftplusOpMaker, softplus_grad,
 REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad,
             ops::ActivationOpGrad);
 
-REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker<float>, brelu_grad,
+REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker, brelu_grad,
             ops::ActivationOpGrad);
 
-REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker<float>,
+REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker,
             leaky_relu_grad, ops::ActivationOpGrad);
 
-REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker<float>,
-            soft_relu_grad, ops::ActivationOpGrad);
+REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker, soft_relu_grad,
+            ops::ActivationOpGrad);
 
-REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker<float>, elu_grad,
+REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker, elu_grad,
             ops::ActivationOpGrad);
 
-REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker<float>, relu6_grad,
+REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker, relu6_grad,
             ops::ActivationOpGrad);
 
-REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker<float>, pow_grad,
+REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker, pow_grad,
             ops::ActivationOpGrad);
 
-REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
+REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker, stanh_grad,
             ops::ActivationOpGrad);
 
-REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker<float>,
+REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker,
             hard_shrink_grad, ops::ActivationOpGrad);
 
-REGISTER_OP(thresholded_relu, ops::ActivationOp,
-            ops::ThresholdedReluOpMaker<float>, thresholded_relu_grad,
-            ops::ActivationOpGrad);
+REGISTER_OP(thresholded_relu, ops::ActivationOp, ops::ThresholdedReluOpMaker,
+            thresholded_relu_grad, ops::ActivationOpGrad);
 
-REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker<float>,
+REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker,
             hard_sigmoid_grad, ops::ActivationOpGrad);
 
 #define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor)       \

paddle/operators/adadelta_op.cc

@@ -109,4 +109,5 @@ paramOut = param + paramUpdate$$
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker);
 REGISTER_OP_CPU_KERNEL(
-    adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>);
+    adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>,
+    ops::AdadeltaOpKernel<paddle::platform::CPUPlace, double>);

paddle/operators/adadelta_op.cu

@@ -17,4 +17,5 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
-    adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>);
+    adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>,
+    ops::AdadeltaOpKernel<paddle::platform::GPUPlace, double>);

paddle/operators/adadelta_op.h

@@ -33,8 +33,8 @@ class AdadeltaOpKernel : public framework::OpKernel<T> {
     avg_squared_grad_out_tensor->mutable_data<T>(ctx.GetPlace());
     avg_squared_update_out_tensor->mutable_data<T>(ctx.GetPlace());
 
-    float rho = ctx.Attr<float>("rho");
-    float epsilon = ctx.Attr<float>("epsilon");
+    T rho = static_cast<T>(ctx.Attr<float>("rho"));
+    T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
 
     auto param = framework::EigenVector<T>::Flatten(
         *ctx.Input<framework::Tensor>("Param"));

paddle/operators/adagrad_op.cu

@@ -14,8 +14,8 @@
 
 #define EIGEN_USE_GPU
 #include "paddle/operators/adagrad_op.h"
-#include "paddle/operators/math/selected_rows_functor.h"
 #include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/selected_rows_functor.h"
 #include "paddle/platform/cuda_helper.h"
 
 namespace paddle {
@@ -134,8 +134,8 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
         T, 256><<<grid2, threads, 0,
                   reinterpret_cast<const platform::CUDADeviceContext&>(context)
                       .stream()>>>(grad_merge_data, grad_merge->rows().data(),
-                                   lr, param_data,
-                                   moment_data, grad_width, epsilon);
+                                   lr, param_data, moment_data, grad_width,
+                                   epsilon);
   }
 };
 

paddle/operators/adam_op.cc

@@ -127,4 +127,5 @@ paramOut = param - learningRate * moment_1/ ($\sqrt{(moment_2)} + \epsilon)$$
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker);
 REGISTER_OP_CPU_KERNEL(adam,
-                       ops::AdamOpKernel<paddle::platform::CPUPlace, float>);
+                       ops::AdamOpKernel<paddle::platform::CPUPlace, float>,
+                       ops::AdamOpKernel<paddle::platform::CPUPlace, double>);

paddle/operators/adam_op.cu

@@ -17,4 +17,5 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(adam,
-                       ops::AdamOpKernel<paddle::platform::GPUPlace, float>);
+                       ops::AdamOpKernel<paddle::platform::GPUPlace, float>,
+                       ops::AdamOpKernel<paddle::platform::GPUPlace, double>);

paddle/operators/adam_op.h

@@ -31,9 +31,9 @@ class AdamOpKernel : public framework::OpKernel<T> {
     moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
     moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
 
-    float beta1 = ctx.Attr<float>("beta1");
-    float beta2 = ctx.Attr<float>("beta2");
-    float epsilon = ctx.Attr<float>("epsilon");
+    T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
+    T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
+    T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
 
     auto param = framework::EigenVector<T>::Flatten(
         *ctx.Input<framework::Tensor>("Param"));

paddle/operators/adamax_op.cc

@@ -126,4 +126,5 @@ division by 0 error.
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker);
 REGISTER_OP_CPU_KERNEL(adamax,
-                       ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>);
+                       ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>,
+                       ops::AdamaxOpKernel<paddle::platform::CPUPlace, double>);

paddle/operators/adamax_op.cu

@@ -17,4 +17,5 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(adamax,
-                       ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>);
+                       ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>,
+                       ops::AdamaxOpKernel<paddle::platform::GPUPlace, double>);